Ultrahigh dimensional data with both categorical responses and categorical covariates are frequently encountered in the analysis of big data, for which feature screening has become an indispensable statistical tool. We propose a Pearson chi-square based feature screening procedure for categorical response with ultrahigh dimensional categorical covariates. The proposed procedure can be directly applied for detection of important interaction effects. We further show that the proposed procedure possesses screening consistency property in the terminology of Fan and Lv (2008). We investigate the finite sample performance of the proposed procedure by Monte Carlo simulation studies, and illustrate the proposed method by two empirical datasets.
The contrast medium (CM) induced nephropathy required new CT imaging protocol. This study evaluated the feasibility of low contrast medium (CM) volume and injection flow using aortic dual-energy CT (DECT) angiography with non-linear blending technique. Sixty patients were randomly assigned to two groups: control group (n=30), single-energy CT 70 ml CM at injection rate of 5 ml/s; study group (n=30), DECT mode, 0.5 ml per kg of patient weight CM at injection rate=(weight × 0.5 ml/kg)/(4+scan time). Non-linear blending technique was used for dual-energy images. Mean attention, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of aorta were compared. The level of visible renal artery branches was scored. There was no significant difference between the two groups in the mean aortic attention, SNR and CNR (all P > 0.05). Significant difference was showed in CM injection rate (p < 0.001) and volume (P < 0.001). The renal artery score had no statistically significant difference (P=0.771). Compared conventional scan and CM injection protocol, DECT with non-linear blending technique maintained the image quality of aortic CT angiography with reduced CM volume and flow rate, which could reduce the risks associated with CM injection.
Synthesis of two-dimensional (2D) metal chalcogenide based half-metallic nanosheets is in high demand for modern electronics and spintronics applications. Herein, we predict from first-principles calculations that the 2D heterostructure Co/MoS2, consisting of a monolayer of Co atoms deposited on a single MoS2 sheet, possesses robust ferromagnetic and half-metallic features and exhibits 100% spin-filter efficiency within a broad bias range. Its ferromagnetic and half-metallic nature persists even when overlaid with a graphene sheet. Because of the relatively strong surface binding energy and low clustering ratio of Co atoms on the MoS2 surface, we predict that the heterostructure is synthesizable via wetting deposition of Co on MoS2 by electron-beam evaporation technique. Our work strongly suggests Co/MoS2 as a compelling and feasible candidate for highly effective information and high-density memory devices.
To investigate the image quality and the minimum required radiation dose for automatic tube potential selection (ATPS) in dual-source computed tomography (DSCT) coronary computed tomography angiography (CCTA). Three hundred twenty-five consecutive patients (153 men and 172 women) undergoing CCTA were assigned to either the ATPS group (n = 172) or the control group (n = 153); the control group underwent imaging at a constant current of 120 kV. All patients were scanned in either prospectively ECG-triggered high-pitch helical mode or sequential mode. The subjective image quality score, attenuation, image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), volume CT dose index (CTDIvol), and effective dose (ED) were compared between the two groups with the Student t test or Mann-Whitney U test. The subjective image quality score was not significantly different between the two groups. Imaging noise and attenuation were both significantly higher in the ATPS group than in the control group (imaging noise: 25.6 ± 7.6 versus 15.8 ± 4.0 HU, P < 0.001; attenuation: 559.6 ± 142.0 versus 412.5 ± 64.3 HU, P < 0.001). SNR and CNR were significantly lower in the ATPS group than in the control group (SNR: 23.21 ± 7.40 versus 27.71 ± 8.25, P < 0.001; CNR: 27.81 ± 8.44 versus 33.94 ± 9.69, P < 0.001). ED was significantly lower in the ATPS group than in the control group (ED: 1.25 ± 1.24 versus 2.19 ± 1.77 mSv, P < 0.001). For both groups, ED was significantly lower in the high-pitch mode than in the sequential mode. The use of ATPS for CCTA significantly reduced the radiation dose while maintaining image quality.
We examine a test of a nonparametric regression function based on penalized spline smoothing. We show that, similarly to a penalized spline estimator, the asymptotic power of the penalized spline test falls into a small- K or a large-K scenarios characterized by the number of knots K and the smoothing parameter. However, the optimal rate of K and the smoothing parameter maximizing power for testing is different from the optimal rate minimizing the mean squared error for estimation. Our investigation reveals that compared to estimation, some under-smoothing may be desirable for the testing problems. Furthermore, we compare the proposed test with the likelihood ratio test (LRT). We show that when the true function is more complicated, containing multiple modes, the test proposed here may have greater power than LRT. Finally, we investigate the properties of the test through simulations and apply it to two data examples.
We study non-parametric regression function estimation for models with strong dependence. Compared with short-range dependent models, long-range dependent models often result in slower convergence rates. We propose a simple differencing-sequence based non-parametric estimator that achieves the same convergence rate as if the data were independent. Simulation studies show that the proposed method has good finite sample performance.
Motivated by a study on factors affecting the level of photosynthetic activity in a natural ecosystem, we propose nonlinear varying coefficient models, in which the relationship between the predictors and the response variable is allowed to be nonlinear. One-step local linear estimators are developed for the nonlinear varying coefficient models and their asymptotic normality is established leading to point-wise asymptotic confidence bands for the coefficient functions. Two-step local linear estimators are also proposed for cases where the varying coefficient functions admit different degrees of smoothness; bootstrap confidence intervals are utilized for inference based on the two-step estimators. We further propose a generalized F test to study whether the coefficient functions vary over a covariate. We illustrate the proposed methodology via an application to an ecology data set and study the finite sample performance by Monte Carlo simulation studies.
When the functional data are not homogeneous, e.g., there exist multiple classes of functional curves in the dataset, traditional estimation methods may fail. In this paper, we propose a new estimation procedure for the Mixture of Gaussian Processes, to incorporate both functional and inhomogeneous properties of the data. Our method can be viewed as a natural extension of high-dimensional normal mixtures. However, the key difference is that smoothed structures are imposed for both the mean and covariance functions. The model is shown to be identifiable, and can be estimated efficiently by a combination of the ideas from EM algorithm, kernel regression, and functional principal component analysis. Our methodology is empirically justified by Monte Carlo simulations and illustrated by an analysis of a supermarket dataset.
Theories of nicotine addiction emphasize the initial role of positive reinforcement in the development of regular smoking behavior, and the role of negative reinforcement at later stages. These theories are tested here by examining the effects of amount smoked per smoking event on smoking-related mood changes, and how nicotine dependence (ND) moderates this effect. The current study examines these questions within a sample of light adolescent smokers drawn from the metropolitan Chicago area (N=151, 55.6% female, mean 17.7years).
Combining magnetic resonance imaging (MRI) with near-infrared spectroscopy (NIRS) leads to excellent synergies which can improve the interpretation of either method and can provide novel data with respect to measuring brain oxygenation and metabolism. MRI has good spatial resolution, can detect a range of physiological parameters and is sensitive to changes in deoxyhemoglobin content. NIRS has lower spatial resolution, but can detect, and with specific technologies, quantify, deoxyhemoglobin, oxyhemoglobin, total hemoglobin and cytochrome oxidase. This paper reviews the application of both methods, as a multimodal technology, for assessing changes in brain oxygenation that may occur with changes in functional activation state or metabolic rate. Examples of hypoxia and ischemia are shown. Data support the concept of reduced metabolic rate resulting from hypoxia/ischemia and that metabolic rate in brain is not close to oxygen limitation during normoxia. We show that multimodal MRI and NIRS can provide novel information for studies of brain metabolism.
Researchers have increasingly begun to gather ecological momentary assessment (EMA) data on smoking, but new statistical methods are necessary to fully unlock information from such data. In this paper, we use a new technique, the logistic time-varying effect model (logistic TVEM), to examine the odds of smoking in the 2 weeks after a quit attempt.
This paper is concerned with feature screening and variable selection for varying coefficient models with ultrahigh dimensional covariates. We propose a new feature screening procedure for these models based on conditional correlation coefficient. We systematically study the theoretical properties of the proposed procedure, and establish their sure screening property and the ranking consistency. To enhance the finite sample performance of the proposed procedure, we further develop an iterative feature screening procedure. Monte Carlo simulation studies were conducted to examine the performance of the proposed procedures. In practice, we advocate a two-stage approach for varying coefficient models. The two stage approach consists of (a) reducing the ultrahigh dimensionality by using the proposed procedure and (b) applying regularization methods for dimension-reduced varying coefficient models to make statistical inferences on the coefficient functions. We illustrate the proposed two-stage approach by a real data example.
To investigate the diagnostic accuracy of coronary computed tomographic (CT) angiography (CCTA) using filtered back projection (FBP) and sinogram-affirmed iterative reconstruction (SAFIRE) of different strength factors with invasive coronary angiography as the reference standard.
The timeline follow-back (TLFB) interview was adopted to collect retrospective data on daily substance use and violence from 598 youth seeking care in an urban Emergency Department in Flint, Michigan during 2009-2011. Generalized linear mixed models with flexible smooth functions of time were employed to characterize the change in risk behaviors as a function of the length of recall period. Our results suggest that the 1-week recall period may be more effective for capturing atypical or variable patterns of risk behaviors, whereas a recall period longer than 2 weeks may result in a more stable estimation of a typical pattern.
Multiple sclerosis (MS) impairs brain activity through demyelination and loss of axons. Increased brain activity is accompanied by increases in microvascular hemoglobin oxygen saturation (oxygenation) and total hemoglobin, which can be measured using functional near-infrared spectroscopy (fNIRS). Due to the potentially reduced size and integrity of the white matter tracts within the corpus callosum, it may be expected that MS patients have reduced functional communication between the left and right sides of the brain; this could potentially be an indicator of disease progression. To assess interhemispheric communication in MS, we used fNIRS during a unilateral motor task and the resting state. The magnitude of the change in hemoglobin parameters in the motor cortex was significantly reduced in MS patients during the motor task relative to healthy control subjects. There was also a significant decrease in interhemispheric communication between the motor cortices (expressed as coherence) in MS patients compared to controls during the motor task, but not during the resting state. fNIRS assessment of interhemispheric coherence during task execution may be a useful marker in disorders with white matter damage or axonal loss, including MS.
Quantifying multiple phenotypic aspects of individual craniofacial bones across early osteogenesis illustrates differences in typical bone growth and maturation and provides a basis for understanding the localized and overall influence of mutations associated with disease. We quantify the typical pattern of bone growth and maturation during early craniofacial osteogenesis and determine how this pattern is modified in Fgfr2(+/P253R) Apert syndrome mice.
Mitochondrial DNA (mtDNA) D-loop sequences of 666 individuals (including 109 new individuals, 557 individuals retrieved from GenBank) from 33 Chinese domestic goat breeds throughout China were used to investigate their mtDNA variability and molecular phylogeography. The results showed that all goat breeds in this study proved to be extremely diverse, and the average haplotype diversity and nucleotide diversity were 0.990 ± 0.001 and 0.032 ± 0.001, respectively. The 666 sequences gave 326 different haplotypes. Phylogenetic analyses revealed that there were 4 mtDNA haplogroups identified in Chinese domestic goats, in which haplogroup A was predominant and widely distributed. Our finding was consistent with archaeological data and other genetic diversity studies. Amova analysis showed there was significant geographical structuring. Almost 84.31% of genetic variation was included in the within-breed variance component and only 4.69% was observed among the geographic distributions. This genetic diversity results further supported the previous view of multiple maternal origins of Chinese domestic goats, and the results on the phylogenetic relationship contributed to a better understanding of the history of goat domestication and modern production of domestic goats.
Due to the increasing concern of viral infection related to berries, this study investigated strategies to enhance high hydrostatic pressure (HHP) inactivation of murine norovirus 1 (MNV-1), a human norovirus (HuNoV) surrogate, on strawberries and in strawberry puree. Strawberry puree was inoculated with ~10(6)PFU/g of MNV-1 and treated at 350 MPa for 2 min at initial sample temperatures of 0, 5, 10 and 20°C. MNV-1 became more sensitive to HHP as initial sample temperature decreased from 20 to 0°C. To determine the effect of pressure cycling on MNV-1 inactivation, inoculated puree samples were treated at 300 MPa and 0°C with 1, 2 and 4 cycles. Pressure cycling offered no distinct advantage over continuous HHP treatment. To determine the effect of presence of water during HHP on MNV-1 inactivation, strawberries inoculated with ~ 4 × 10(5)PFU/g of MNV-1 were either pressure-treated directly (dry state) or immersed in water during pressure treatment. MNV-1 was very resistant to pressure under the dry state condition, but became sensitive to pressure under the wet state condition. The inactivation curves of MNV-1 in strawberry puree and on strawberries were obtained at 300 and 350 MPa and initial sample temperature of 0°C. Except for the curve of strawberries treated at 350 MPa which had a concave downward shape, the other three curves were almost linear with R(2) value of 0.99. The fate of MNV-1 in the un-treated and pressure-treated strawberries and strawberry puree during frozen storage was determined. The virus was relatively stable and only reduced by <1.2 log during the 28-day frozen storage. In all, this study provides practical insights of designing strategies using HHP to inactivate HuNoV on strawberries and in strawberry puree assuming that HuNoV behaved similarly to MNV-1 when treated by HHP.
Insulin-like growth factor binding protein-1 (IGFBP-1) plays an important role in the development and progression of cancer. However, the expression of IGFBP-1 remains equivocal, and little is known about its clinicopathological significance and prognostic value in hepatocellular carcinoma (HCC). In this study, we evaluated the expression of IGFBP-1 in 90 paired HCC tissues and adjacent non-cancerous liver tissues and analyzed its clinical and prognostic significance. The results showed that IGFBP-1 was detected in cytoplasm as well as cell nucleus, and down-regulated in HCC tissues compared to the adjacent non-cancerous liver tissues. The decreased expression of IGFBP-1 was correlated with tumor differentiation, liver cirrhosis, microvascular invasion or metastasis, TNM stage and poor survival. Moreover, low levels of IGFBP-1 may be an independent prognostic indicator for the survival of patients with HCC. We also evaluated its function by adding recombinant IGFBP-1 to the cultured HCC cell lines HepG2 and MHCC97-H. The result of the invasion chamber assay showed that IGFBP-1 could inhibit the invasion of HepG2 and MHCC97-H. MMP-9 secretion by these cells was significantly decreased when the cells were treated with IGFBP-1. Our results suggest that IGFBP-1 inhibits the invasion and metastasis of HCC cells and that IGFBP-1 may be useful as a valuable marker for the prognosis of patients with HCC.
Motivated by an analysis of US house price index data, we propose nonparametric finite mixture of regression models. We study the identifiability issue of the proposed models, and develop an estimation procedure by employing kernel regression. We further systematically study the sampling properties of the proposed estimators, and establish their asymptotic normality. A modified EM algorithm is proposed to carry out the estimation procedure. We show that our algorithm preserves the ascent property of the EM algorithm in an asymptotic sense. Monte Carlo simulations are conducted to examine the finite sample performance of the proposed estimation procedure. An empirical analysis of the US house price index data is illustrated for the proposed methodology.
Objective: The goal of this article is to introduce to social and behavioral scientists the generalized time-varying effect model (TVEM), a semiparametric approach for investigating time-varying effects of a treatment. The method is best suited for data collected intensively over time (e.g., experience sampling or ecological momentary assessments) and addresses questions pertaining to effects of treatment changing dynamically with time. Thus, of interest is the description of timing, magnitude, and (nonlinear) patterns of the effect. Method: Our presentation focuses on practical aspects of the model. A step-by-step demonstration is presented in the context of an empirical study designed to evaluate effects of surgical treatment on quality of life among early stage lung cancer patients during posthospitalization recovery (N = 59; 61% female, M age = 66.1 years). Frequency and level of distress associated with physical symptoms were assessed twice daily over a 2-week period, providing a total of 1,544 momentary assessments. Results: Traditional analyses (analysis of covariance [ANCOVA], repeated-measures ANCOVA, and multilevel modeling) yielded findings of no group differences. In contrast, generalized TVEM identified a pattern of the effect that varied in time and magnitude. Group differences manifested after Day 4. Conclusions: Generalized TVEM is a flexible statistical approach that offers insight into the complexity of treatment effects and allows modeling of nonnormal outcomes. The practical demonstration, shared syntax, and availability of a free set of macros aim to encourage researchers to apply TVEM to complex data and stimulate important scientific discoveries. (PsycINFO Database Record (c) 2013 APA, all rights reserved).
Mesoporous single crystal rutile TiO2 with clear facets was prepared by a seeded template method. As a result of unique microstructure, the resulting TiO2 exhibits remarkably improved photocatalytic and photoelectrochemical activities in hydrogen or oxygen evolution.
Human T-cell leukemia virus type 1 (HTLV-1) is causally associated with adult T-cell leukemia (ATL), an aggressive T-cell malignancy with a poor prognosis. To elucidate ATL pathogenesis in vivo, a variety of animal models have been established; however, the mechanisms driving this disorder remain poorly understood due to deficiencies in each of these animal models. Here, we report a novel HTLV-1-infected humanized mouse model generated by intra-bone marrow injection of human CD133(+) stem cells into NOD/Shi-scid/IL-2R?c null (NOG) mice (IBMI-huNOG mice). Upon infection, the number of CD4(+) human T cells in the periphery increased rapidly, and atypical lymphocytes with lobulated nuclei resembling ATL-specific flower cells were observed 4 to 5 months after infection. Proliferation was seen in both CD25(-) and CD25(+) CD4 T cells with identical proviral integration sites; however, a limited number of CD25(+)-infected T-cell clones eventually dominated, indicating an association between clonal selection of infected T cells and expression of CD25. Additionally, HTLV-1-specific adaptive immune responses were induced in infected mice and might be involved in the control of HTLV-1-infected cells. Thus, the HTLV-1-infected IBMI-huNOG mouse model successfully recapitulated the development of ATL, and may serve as an important tool for investigating in vivo mechanisms of ATL leukemogenesis and evaluating anti-ATL drug and vaccine candidates.
Efficient new technology has made it straightforward for behavioral scientists to collect anywhere from several dozen to several thousand dense, repeated measurements on one or more time-varying variables. These intensive longitudinal data (ILD) are ideal for examining complex change over time but present new challenges that illustrate the need for more advanced analytic methods. For example, in ILD the temporal spacing of observations may be irregular, and individuals may be sampled at different times. Also, it is important to assess both how the outcome changes over time and the variation between participants time-varying processes to make inferences about a particular interventions effectiveness within the population of interest. The methods presented in this article integrate 2 innovative ILD analytic techniques: functional data analysis and dynamical systems modeling. An empirical application is presented using data from a smoking cessation clinical trial. Study participants provided 42 daily assessments of pre-quit and post-quit withdrawal symptoms. Regression splines were used to approximate smooth functions of craving and negative affect and to estimate the variables derivatives for each participant. We then modeled the dynamics of nicotine craving using standard input-output dynamical systems models. These models provide a more detailed characterization of the post-quit craving process than do traditional longitudinal models, including information regarding the type, magnitude, and speed of the response to an input. The results, in conjunction with standard engineering control theory techniques, could potentially be used by tobacco researchers to develop a more effective smoking intervention. (PsycINFO Database Record (c) 2013 APA, all rights reserved).
Pentatricopeptide repeat (PPR) proteins are sequence-specific RNA-binding proteins that form a pervasive family of proteins conserved in yeast, plants, and humans. The plant PPR proteins are grouped mainly into the P and PLS classes. Here, we report the crystal structure of a PLS-class PPR protein from Arabidopsis thaliana called THA8L (THA8-like) at 2.0 ?. THA8L resembles THA8 (thylakoid assembly 8), a protein that is required for the splicing of specific group II introns of genes involved in biogenesis of chloroplast thylakoid membranes. The THA8L structure contains three P-type PPR motifs flanked by one L-type motif and one S-type motif. We identified several putative THA8L-binding sites, enriched with purine sequences, in the group II introns. Importantly, THA8L has strong binding preference for single-stranded RNA over single-stranded DNA or double-stranded RNA. Structural analysis revealed that THA8L contains two extensive patches of positively charged residues next to the residues that are proposed to comprise the RNA-binding codes. Mutations in these two positively charged patches greatly reduced THA8L RNA-binding activity. On the basis of these data, we constructed a model of THA8L-RNA binding that is dependent on two forces: one is the interaction between nucleotide bases and specific amino acids in the PPR motifs (codes), and the other is the interaction between the negatively charged RNA backbone and positively charged residues of PPR motifs. Together, these results further our understanding of the mechanism of PPR protein-RNA interactions.
Advancing understanding of smoking cessation requires a complex and nuanced understanding of behavior change. To this end, ecological momentary assessments (EMA) are now being collected extensively. The time-varying effect model (TVEM) is a statistical technique ideally suited to model processes that unfold as behavior and nicotine dependence change. Coefficients are expressed dynamically over time and represented as smooth functions of time.
Ecological momentary assessments (EMA) are increasingly used in studies of smoking behavior. Through EMA, examination of lagged relationships is particularly useful for establishing a temporal order of events and for identifying types and timing of risk factors. The time-varying effect model (TVEM) handles EMA data challenges and addresses unique questions about the time-varying effects.
To understand the dynamic process of cessation fatigue (i.e., the tiredness of trying to quit smoking) with respect to its average trend, effect on relapse, time-varying relations with craving and negative affect, and differences among genders and treatment groups.
The reflectivity of Al/Zr multilayers is enhanced by the use of a novel structure. The Al layers are divided by insertion of Si layers. In addition, Si barrier layers are inserted at the Al/Zr interfaces (Zr-on-Al and Al-on-Zr). As a result, crystallization of the Al layer is inhibited and that of Zr is enhanced. In grazing incidence x-ray reflectometry, x-ray diffraction, and extreme ultraviolet measurements, the novel multilayers exhibit lower interfacial roughness compared with traditional multilayer structures, and their reflectivity is increased from 48.2% to 50.0% at a 5° angle of incidence. These novel multilayers also have potential applications in other multilayer systems and the semiconductor industry.
Mixed-effects models have recently become popular for analyzing sparse longitudinal data that arise naturally in biological, agricultural and biomedical studies. Traditional approaches assume independent residuals over time and explain the longitudinal dependence by random effects. However, when bivariate or multivariate traits are measured longitudinally, this fundamental assumption is likely to be violated because of intertrait dependence over time. We provide a more general framework where the dependence of the observations from the same subject over time is not assumed to be explained completely by the random effects of the model. We propose a novel, mixed model-based approach and estimate the error-covariance structure nonparametrically under a generalized linear model framework. We use penalized splines to model the general effect of time, and we consider a Dirichlet process mixture of normal prior for the random-effects distribution. We analyze blood pressure data from the Framingham Heart Study where body mass index, gender and time are treated as covariates. We compare our method with traditional methods including parametric modeling of the random effects and independent residual errors over time. We conduct extensive simulation studies to investigate the practical usefulness of the proposed method. The current approach is very helpful in analyzing bivariate irregular longitudinal traits.
Although the mechanism of superconductivity in the cuprates remains elusive, it is generally agreed that at the heart of the problem is the physics of doped Mott insulators. A crucial step for solving the high temperature superconductivity puzzle is to elucidate the electronic structure of the parent compound and the behaviour of doped charge carriers. Here we use scanning tunnelling microscopy to investigate the atomic-scale electronic structure of the Ca(2)CuO(2)Cl(2) parent Mott insulator of the cuprates. The full electronic spectrum across the Mott-Hubbard gap is uncovered for the first time, which reveals the particle-hole symmetric and spatially uniform Hubbard bands. Defect-induced charge carriers are found to create broad in-gap electronic states that are strongly localized in space. We show that the electronic structure of pristine Mott insulator is consistent with the Zhang-Rice singlet model, but the peculiar features of the doped electronic states require further investigations.
Angiogenesis is a hallmark of many conditions, including cancer, stroke, vascular disease, diabetes, and high-altitude exposure. We have previously shown that one can study angiogenesis in animal models by using total hemoglobin (tHb) as a marker of cerebral blood volume (CBV), measured using broadband near-infrared spectroscopy (bNIRS). However, the method was not suitable for patients as global anoxia was used for the calibration. Here we determine if angiogenesis could be detected using a calibration method that could be applied to patients. CBV, as a marker of angiogenesis, is quantified in a rat cortex before and after hypoxia acclimation. Rats are acclimated at 370-mmHg pressure for three weeks, while rats in the control group are housed under the same conditions, but under normal pressure. CBV increased in each animal in the acclimation group. The mean CBV (%volume/volume) is 3.49%± 0.43% (mean ± SD) before acclimation for the experimental group, and 4.76%± 0.29% after acclimation. The CBV for the control group is 3.28%± 0.75%, and 3.09%± 0.48% for the two measurements. This demonstrates that angiogenesis can be monitored noninvasively over time using a bNIRS system with a calibration method that is compatible with human use and less stressful for studies using animals.
To individually optimize contrast medium protocol for high-pitch prospective ECG-triggering coronary CT angiography using body weight. Ninety patients undergoing high-pitch coronary CT angiography were randomly assigned to 3 contrast medium injection protocols with bolus tracking technique: Group A, 0.7 ml CM per kg patient weight (ml/kg); Group B, 0.6 ml/kg; Group C, 0.5 ml/kg. Each group had 30 patients. The CT values of superior vena cava (SVC), pulmonary artery (PA), ascending aorta (AA), left atrium (LA), left ventricle (LV), left main artery (LM) and proximal segment of right coronary artery (RCA) were measured. The image quality of coronary artery was evaluated on per-segment basis using a 4-point scale (1-excellent, 4-non-diagnosis). The CT value was not significantly different on AA (p = 0.735), LM (p = 0.764), and proximal segment of RCA (p = 0.991). The CT value was significantly different on SVC, PA, LA and LV (all p < 0.05). The mean image quality score was 1.6 ± 0.1, 1.6 ± 0.1 and 1.6 ± 0.1 (p = 0.217). The volume of CM was 47 ± 8, 44 ± 8 and 36 ± 6 ml for 3 groups (p < 0.001). The effective radiation dose was 0.88 ± 0.04, 0.87 ± 0.06, and 0.85 ± 0.07 mSv for 3 groups. Contrast medium could be reduced to 0.5 ml/kg for high-pitch coronary CT angiography without compromising diagnostic image quality, which associated ~50 % reduction of total contrast volume compared with standard contrast protocol with test bolus technique.
The synthesis of novel purine nucleosides-linked azacrown ethers in the C6 position, N-(2-chloro purin-6-yl) aza-18-crown-6 (NCPAC), was described. This new nucleoside analogue can be prepared from a series of N9-modified nucleosides and the method allows for new and easy modification of the nucleosides. The interaction between NCPAC and human serum albumin (HSA) was studied using molecular docking and fluorescence techniques. Thermodynamics revealed that the interaction was entropy driven with predominantly hydrophobic forces. From the observed Fösters-type fluorescence resonance energy transfer, the donor (Trp 214 in HSA) to acceptor (NCPAC) distance was calculated to be 3.6 nm. The conformational changes of HSA due to the interaction were investigated qualitatively from synchronous fluorescence spectra. Molecular docking studies were performed to obtain information on the possible residues involved in the interaction process.
Artemisinin, a sesquiterpene lactone endoperoxide derived from Artemisia annua L. (Asteraceae), is the most effective antimalarial drug. We used two methods: genome walking and thermal asymmetric interlaced polymerase chain reaction, to isolate the unknown 5-flanking sequence of the cyp71av1 gene. The subsequent sequence analysis using bioinformatics software revealed that there are several cis-acting elements inside the cyp71av1 promoter. The 5-rapid amplification of the cDNA ends method was used to determine the transcription start site of the cyp71av1 gene. We then mapped it at the 18 base upstream of the ATG initiation codon. For simple functional characterization, we built fusion vectors between the 5-deletion promoter and the gas reporter gene. The expression levels of the transferred vectors into A. annua L. were analyzed by the transient expression way. The beta-glucuronidase assay results indicated that deletion of the region to -1551 bp did not lead to much damage in the GUS activity, whereas further deletion, to -1155 bp, resulted in a 5.5-fold reduction of GUS activity. In stabilized transgenic A. annua L. seedlings we observed that GUS expression was restricted to trichomes, which means that the promoter of the cyp71av1 gene is trichome-specific. Compared with the constitutive CaMV 35S promoter, which can express genes throughout the plant, influence on the trichome system through the trichome-specific expression promoter merely imperils plant growth. In addition, the promoter of the cyp71av1 gene contains several binding sites for transcription factors, which implies that the cyp71av1 promoter responds to more than one form of stimulation.
Understanding temporal change in human behavior and psychological processes is a central issue in the behavioral sciences. With technological advances, intensive longitudinal data (ILD) are increasingly generated by studies of human behavior that repeatedly administer assessments over time. ILD offer unique opportunities to describe temporal behavioral changes in detail and identify related environmental and psychosocial antecedents and consequences. Traditional analytical approaches impose strong parametric assumptions about the nature of change in the relationship between time-varying covariates and outcomes of interest. This article introduces time-varying effect models (TVEMs) that explicitly model changes in the association between ILD covariates and ILD outcomes over time in a flexible manner. In this article, we describe unique research questions that the TVEM addresses, outline the model-estimation procedure, share a SAS macro for implementing the model, demonstrate model utility with a simulated example, and illustrate model applications in ILD collected as part of a smoking-cessation study to explore the relationship between smoking urges and self-efficacy during the course of the pre- and postcessation period.
In family-based longitudinal genetic studies, investigators collect repeated measurements on a trait that changes with time along with genetic markers. Since repeated measurements are nested within subjects and subjects are nested within families, both the subject-level and measurement-level correlations must be taken into account in the statistical analysis to achieve more accurate estimation. In such studies, the primary interests include to test for quantitative trait locus (QTL) effect, and to estimate age-specific QTL effect and residual polygenic heritability function. We propose flexible semiparametric models along with their statistical estimation and hypothesis testing procedures for longitudinal genetic designs. We employ penalized splines to estimate nonparametric functions in the models. We find that misspecifying the baseline function or the genetic effect function in a parametric analysis may lead to substantially inflated or highly conservative type I error rate on testing and large mean squared error on estimation. We apply the proposed approaches to examine age-specific effects of genetic variants reported in a recent genome-wide association study of blood pressure collected in the Framingham Heart Study.
Organisms usually cope with change in the environment by altering the dynamic trajectory of gene expression to adjust the complement of active proteins. The identification of particular sets of genes whose expression is adaptive in response to environmental changes helps to understand the mechanistic base of gene-environment interactions essential for organismic development. We describe a computational framework for clustering the dynamics of gene expression in distinct environments through Gaussian mixture fitting to the expression data measured at a set of discrete time points. We outline a number of quantitative testable hypotheses about the patterns of dynamic gene expression in changing environments and gene-environment interactions causing developmental differentiation. The future directions of gene clustering in terms of incorporations of the latest biological discoveries and statistical innovations are discussed. We provide a set of computational tools that are applicable to modeling and analysis of dynamic gene expression data measured in multiple environments.
We consider the recent history functional linear models, relating a longitudinal response to a longitudinal predictor where the predictor process only in a sliding window into the recent past has an effect on the response value at the current time. We propose an estimation procedure for recent history functional linear models that is geared towards sparse longitudinal data, where the observation times across subjects are irregular and total number of measurements per subject is small. The proposed estimation procedure builds upon recent developments in literature for estimation of functional linear models with sparse data and utilizes connections between the recent history functional linear models and varying coefficient models. We establish uniform consistency of the proposed estimators, propose prediction of the response trajectories and derive their asymptotic distribution leading to asymptotic point-wise confidence bands. We include a real data application and simulation studies to demonstrate the efficacy of the proposed methodology.
The complexity of semiparametric models poses new challenges to statistical inference and model selection that frequently arise from real applications. In this work, we propose new estimation and variable selection procedures for the semiparametric varying-coefficient partially linear model. We first study quantile regression estimates for the nonparametric varying-coefficient functions and the parametric regression coefficients. To achieve nice efficiency properties, we further develop a semiparametric composite quantile regression procedure. We establish the asymptotic normality of proposed estimators for both the parametric and nonparametric parts and show that the estimators achieve the best convergence rate. Moreover, we show that the proposed method is much more efficient than the least-squares-based method for many non-normal errors and that it only loses a small amount of efficiency for normal errors. In addition, it is shown that the loss in efficiency is at most 11.1% for estimating varying coefficient functions and is no greater than 13.6% for estimating parametric components. To achieve sparsity with high-dimensional covariates, we propose adaptive penalization methods for variable selection in the semiparametric varying-coefficient partially linear model and prove that the methods possess the oracle property. Extensive Monte Carlo simulation studies are conducted to examine the finite-sample performance of the proposed procedures. Finally, we apply the new methods to analyze the plasma beta-carotene level data.
Being able to wait is an essential part of self-regulation. In the present study, the authors examined the developmental course of changes in the latency to and duration of target-waiting behaviors by following 65 boys and 55 girls from rural and semirural economically strained homes from ages 18 months to 48 months. Age-related changes in latency to and duration of childrens anger expressions and attention focus (e.g., self-initiated distraction) during an 8-min wait for a gift were found. On average, at 18 and 24 months of age, children were quick to react angrily and slower to shift attention away from the desired object than they were at later ages. Over time, children were quicker to distract themselves. By 36 months, distractions occurred before children expressed anger, and anger expressions were briefer. At 48 months, children typically made a quick bid to their mothers about having to wait before distracting themselves; on average, they did not appear angry until the latter half of the wait. Unexpectedly, children bid to their mothers as much at age 48 months as they had at 18 months; however, bids became less angry as children got older. Developmental changes in distraction and bidding predicted age-related changes in the latency to anger. Findings are discussed in terms of the neurocognitive control of attention around age 30 months, the limitations of childrens self-regulatory efforts at age 48 months, and the importance of fostering childrens ability to forestall, as well as modulate, anger.
Zero-inflated count data are very common in health surveys. This study develops new variable selection methods for the zero-inflated Poisson regression model. Our simulations demonstrate the negative consequences which arise from the ignorance of zero-inflation. Among the competing methods, the one-step SCAD method is recommended because it has the highest specificity, sensitivity, exact fit, and lowest estimation error. The design of the simulations is based on the special features of two large national databases commonly used in the alcoholism and substance abuse field so that our findings can be easily generalized to the real settings. Applications of the methodology are demonstrated by empirical analyses on the data from a well-known alcohol study.
Although genome-wide association studies (GWAS) are widely used to identify the genetic and environmental etiology of a trait, several key issues related to their statistical power and biological relevance have remained unexplored. Here, we describe a novel statistical approach, called functional GWAS or fGWAS, to analyze the genetic control of traits by integrating biological principles of trait formation into the GWAS framework through mathematical and statistical bridges. fGWAS can address many fundamental questions, such as the patterns of genetic control over development, the duration of genetic effects, as well as what causes developmental trajectories to change or stop changing. In statistics, fGWAS displays increased power for gene detection by capitalizing on cumulative phenotypic variation in a longitudinal trait over time and increased robustness for manipulating sparse longitudinal data.
Functional mapping is a statistical method for mapping quantitative trait loci (QTLs) that regulate the dynamic pattern of a biological trait. This method integrates mathematical aspects of biological complexity into a mixture model for genetic mapping and tests the genetic effects of QTLs by comparing genotype-specific curve parameters. As a way of quantitatively specifying the dynamic behavior of a system, differential equations have proven to be powerful for modeling and unraveling the biochemical, molecular, and cellular mechanisms of a biological process, such as biological rhythms. The equipment of functional mapping with biologically meaningful differential equations provides new insights into the genetic control of any dynamic processes. We formulate a new functional mapping framework for a dynamic biological rhythm by incorporating a group of ordinary differential equations (ODE). The Runge-Kutta fourth order algorithm was implemented to estimate the parameters that define the system of ODE. The new model will find its implications for understanding the interplay between gene interactions and developmental pathways in complex biological rhythms.
The aim of this paper is to present a functional analysis of a diffusion tensor tract statistics (FADTTS) pipeline for delineating the association between multiple diffusion properties along major white matter fiber bundles with a set of covariates of interest, such as age, diagnostic status and gender, and the structure of the variability of these white matter tract properties in various diffusion tensor imaging studies. The FADTTS integrates five statistical tools: (i) a multivariate varying coefficient model for allowing the varying coefficient functions in terms of arc length to characterize the varying associations between fiber bundle diffusion properties and a set of covariates, (ii) a weighted least squares estimation of the varying coefficient functions, (iii) a functional principal component analysis to delineate the structure of the variability in fiber bundle diffusion properties, (iv) a global test statistic to test hypotheses of interest, and (v) a simultaneous confidence band to quantify the uncertainty in the estimated coefficient functions. Simulated data are used to evaluate the finite sample performance of FADTTS. We apply FADTTS to investigate the development of white matter diffusivities along the splenium of the corpus callosum tract and the right internal capsule tract in a clinical study of neurodevelopment. FADTTS can be used to facilitate the understanding of normal brain development, the neural bases of neuropsychiatric disorders, and the joint effects of environmental and genetic factors on white matter fiber bundles. The advantages of FADTTS compared with the other existing approaches are that they are capable of modeling the structured inter-subject variability, testing the joint effects, and constructing their simultaneous confidence bands. However, FADTTS is not crucial for estimation and reduces to the functional analysis method for the single measure.
With the recent explosion of scientific data of unprecedented size and complexity, feature ranking and screening are playing an increasingly important role in many scientific studies. In this article, we propose a novel feature screening procedure under a unified model framework, which covers a wide variety of commonly used parametric and semiparametric models. The new method does not require imposing a specific model structure on regression functions, and thus is particularly appealing to ultrahigh-dimensional regressions, where there are a huge number of candidate predictors but little information about the actual model forms. We demonstrate that, with the number of predictors growing at an exponential rate of the sample size, the proposed procedure possesses consistency in ranking, which is both useful in its own right and can lead to consistency in selection. The new procedure is computationally efficient and simple, and exhibits a competent empirical performance in our intensive simulations and real data analysis.
In many clinical settings, a commonly encountered problem is to assess accuracy of a screening test for early detection of a disease. In these applications, predictive performance of the test is of interest. Variable selection may be useful in designing a medical test. An example is a research study conducted to design a new screening test by selecting variables from an existing screener with a hierarchical structure among variables: there are several root questions followed by their stem questions. The stem questions will only be asked after a subject has answered the root question. It is therefore unreasonable to select a model that only contains stem variables but not its root variable. In this work, we propose methods to perform variable selection with structured variables when predictive accuracy of a diagnostic test is the main concern of the analysis. We take a linear combination of individual variables to form a combined test. We then maximize a direct summary measure of the predictive performance of the test, the area under a receiver operating characteristic curve (AUC of an ROC), subject to a penalty function to control for overfitting. Since maximizing empirical AUC of the ROC of a combined test is a complicated nonconvex problem (Pepe, Cai, and Longton, 2006, Biometrics62, 221-229), we explore the connection between the empirical AUC and a support vector machine (SVM). We cast the problem of maximizing predictive performance of a combined test as a penalized SVM problem and apply a reparametrization to impose the hierarchical structure among variables. We also describe a penalized logistic regression variable selection procedure for structured variables and compare it with the ROC-based approaches. We use simulation studies based on real data to examine performance of the proposed methods. Finally we apply developed methods to design a structured screener to be used in primary care clinics to refer potentially psychotic patients for further specialty diagnostics and treatment.
The goal of this study is to provide an empirical example using longitudinal cigarette smoking data that compares results of growth mixture trajectory models on the basis of contiguous and snapshot measurements. Data were drawn from an intensive longitudinal study of college freshman (N = 905) with a previous history of smoking. Participants provided weekly smoking reports for 35 consecutive weeks. We found that using contiguous weekly data (35 waves) or 6-wave or 4-wave snapshot data provided similar trajectory curves and proportions. However, there were notable differences in individual trajectory assignments on the basis of contiguous and snapshot measurements.
There has been considerable attention on estimation of conditional variance function in the literature. We propose here a nonparametric model for conditional covariance matrix. A kernel estimator is developed accordingly, its asymptotic bias and variance are derived, and its asymptotic normality is established. A real data example is used to illustrate the proposed estimation procedure.
Despite their success in identifying genes that affect complex disease or traits, current genome-wide association studies (GWASs) based on a single SNP analysis are too simple to elucidate a comprehensive picture of the genetic architecture of phenotypes. A simultaneous analysis of a large number of SNPs, although statistically challenging, especially with a small number of samples, is crucial for genetic modeling.
In partially linear single-index models, we obtain the semiparametrically efficient profile least-squares estimators of regression coefficients. We also employ the smoothly clipped absolute deviation penalty (SCAD) approach to simultaneously select variables and estimate regression coefficients. We show that the resulting SCAD estimators are consistent and possess the oracle property. Subsequently, we demonstrate that a proposed tuning parameter selector, BIC, identifies the true model consistently. Finally, we develop a linear hypothesis test for the parametric coefficients and a goodness-of-fit test for the nonparametric component, respectively. Monte Carlo studies are also presented.
Local polynomial regression is a useful nonparametric regression tool to explore fine data structures and has been widely used in practice. In this paper, we propose a new nonparametric regression technique called local composite-quantile-regression (CQR) smoothing in order to further improve local polynomial regression. Sampling properties of the proposed estimation procedure are studied. We derive the asymptotic bias, variance and normality of the proposed estimate. Asymptotic relative efficiency of the proposed estimate with respect to the local polynomial regression is investigated. It is shown that the proposed estimate can be much more efficient than the local polynomial regression estimate for various non-normal errors, while being almost as efficient as the local polynomial regression estimate for normal errors. Simulation is conducted to examine the performance of the proposed estimates. The simulation results are consistent with our theoretical findings. A real data example is used to illustrate the proposed method.
This paper describes and compares a selection of available modeling techniques for identifying homogeneous population subgroups in the interest of informing targeted substance use intervention. We present a nontechnical review of the common and unique features of three methods: (a) trajectory analysis, (b) functional hierarchical linear modeling (FHLM), and (c) decision tree methods. Differences among the techniques are described, including required data features, strengths and limitations in terms of the flexibility with which outcomes and predictors can be modeled, and the potential of each technique for helping to inform the selection of targets and timing of substance intervention programs.
We apply the nonconcave penalized likelihood approach to obtain variable selections as well as shrinkage estimators. This approach relies heavily on the choice of regularization parameter, which controls the model complexity. In this paper, we propose employing the generalized information criterion (GIC), encompassing the commonly used Akaike information criterion (AIC) and Bayesian information criterion (BIC), for selecting the regularization parameter. Our proposal makes a connection between the classical variable selection criteria and the regularization parameter selections for the nonconcave penalized likelihood approaches. We show that the BIC-type selector enables identification of the true model consistently, and the resulting estimator possesses the oracle property in the terminology of Fan and Li (2001). In contrast, however, the AIC-type selector tends to overfit with positive probability. We further show that the AIC-type selector is asymptotically loss efficient, while the BIC-type selector is not. Our simulation results confirm these theoretical findings, and an empirical example is presented. Some technical proofs are given in the online supplementary material.
Measurement error data or errors-in-variable data are often collected in many studies. Natural criterion functions are often unavailable for general functional measurement error models due to the lack of information on the distribution of the unobservable covariates. Typically, the parameter estimation is via solving estimating equations. In addition, the construction of such estimating equations routinely requires solving integral equations, hence the computation is often much more intensive compared with ordinary regression models. Because of these difficulties, traditional best subset variable selection procedures are not applicable, and in the measurement error model context, variable selection remains an unsolved issue. In this paper, we develop a framework for variable selection in measurement error models via penalized estimating equations. We first propose a class of selection procedures for general parametric measurement error models and for general semiparametric measurement error models, and study the asymptotic properties of the proposed procedures. Then, under certain regularity conditions and with a properly chosen regularization parameter, we demonstrate that the proposed procedure performs as well as an oracle procedure. We assess the finite sample performance via Monte Carlo simulation studies and illustrate the proposed methodology through the empirical analysis of a familiar data set.
Living things come in all shapes and sizes, from bacteria, plants, and animals to humans. Knowledge about the genetic mechanisms for biological shape has far-reaching implications for a range spectrum of scientific disciplines including anthropology, agriculture, developmental biology, evolution and biomedicine.
This article focuses on variable selection for partially linear models when the covariates are measured with additive errors. We propose two classes of variable selection procedures, penalized least squares and penalized quantile regression, using the nonconvex penalized principle. The first procedure corrects the bias in the loss function caused by the measurement error by applying the so-called correction-for-attenuation approach, whereas the second procedure corrects the bias by using orthogonal regression. The sampling properties for the two procedures are investigated. The rate of convergence and the asymptotic normality of the resulting estimates are established. We further demonstrate that, with proper choices of the penalty functions and the regularization parameter, the resulting estimates perform asymptotically as well as an oracle procedure (Fan and Li 2001). Choice of smoothing parameters is also discussed. Finite sample performance of the proposed variable selection procedures is assessed by Monte Carlo simulation studies. We further illustrate the proposed procedures by an application.
In many statistical applications, data are collected over time, and they are likely correlated. In this paper, we investigate how to incorporate the correlation information into the local linear regression. Under the assumption that the error process is an auto-regressive process, a new estimation procedure is proposed for the nonparametric regression by using local linear regression method and the profile least squares techniques. We further propose the SCAD penalized profile least squares method to determine the order of auto-regressive process. Extensive Monte Carlo simulation studies are conducted to examine the finite sample performance of the proposed procedure, and to compare the performance of the proposed procedures with the existing one. From our empirical studies, the newly proposed procedures can dramatically improve the accuracy of naive local linear regression with working-independent error structure. We illustrate the proposed methodology by an analysis of real data set.
Shrinkage-type variable selection procedures have recently seen increasing applications in biomedical research. However, their performance can be adversely influenced by outliers in either the response or the covariate space. This article proposes a weighted Wilcoxon-type smoothly clipped absolute deviation (WW-SCAD) method, which deals with robust variable selection and robust estimation simultaneously. The new procedure can be conveniently implemented with the statistical software R. We establish that the WW-SCAD correctly identifies the set of zero coefficients with probability approaching one and estimates the nonzero coefficients with the rate n(-1/2). Moreover, with appropriately chosen weights the WW-SCAD is robust with respect to outliers in both the x and y directions. The important special case with constant weights yields an oracle-type estimator with high efficiency in the presence of heavier-tailed random errors. The robustness of the WW-SCAD is partly justified by its asymptotic performance under local shrinking contamination. We propose a Bayesian information criterion type tuning parameter selector for the WW-SCAD. The performance of the WW-SCAD is demonstrated via simulations and by an application to a study that investigates the effects of personal characteristics and dietary factors on plasma beta-carotene level.
An investigator who plans to conduct an experiment with multiple independent variables must decide whether to use a complete or reduced factorial design. This article advocates a resource management perspective on making this decision, in which the investigator seeks a strategic balance between service to scientific objectives and economy. Considerations in making design decisions include whether research questions are framed as main effects or simple effects; whether and which effects are aliased (confounded) in a particular design; the number of experimental conditions that must be implemented in a particular design and the number of experimental subjects the design requires to maintain the desired level of statistical power; and the costs associated with implementing experimental conditions and obtaining experimental subjects. In this article 4 design options are compared: complete factorial, individual experiments, single factor, and fractional factorial. Complete and fractional factorial designs and single-factor designs are generally more economical than conducting individual experiments on each factor. Although relatively unfamiliar to behavioral scientists, fractional factorial designs merit serious consideration because of their economy and versatility.
By allowing the regression coefficients to change with certain covariates, the class of varying coefficient models offers a flexible approach to modeling nonlinearity and interactions between covariates. This paper proposes a novel estimation procedure for the varying coefficient models based on local ranks. The new procedure provides a highly efficient and robust alternative to the local linear least squares method, and can be conveniently implemented using existing R software package. Theoretical analysis and numerical simulations both reveal that the gain of the local rank estimator over the local linear least squares estimator, measured by the asymptotic mean squared error or the asymptotic mean integrated squared error, can be substantial. In the normal error case, the asymptotic relative efficiency for estimating both the coefficient functions and the derivative of the coefficient functions is above 96%; even in the worst case scenarios, the asymptotic relative efficiency has a lower bound 88.96% for estimating the coefficient functions, and a lower bound 89.91% for estimating their derivatives. The new estimator may achieve the nonparametric convergence rate even when the local linear least squares method fails due to infinite random error variance. We establish the large sample theory of the proposed procedure by utilizing results from generalized U-statistics, whose kernel function may depend on the sample size. We also extend a resampling approach, which perturbs the objective function repeatedly, to the generalized U-statistics setting; and demonstrate that it can accurately estimate the asymptotic covariance matrix.
The first step in many applications of response surface methodology is typically the screening process. Variable selection plays an important role in screening experiments when a large number of potential factors are introduced in a preliminary study. Traditional approaches, such as the best subset variable selection and stepwise deletion, may not be appropriate in this situation. In this paper we introduce a variable selection procedure via penalized least squares with the SCAD penalty. An algorithm to find the penalized least squares solution is suggested, and a standard error formula for the penalized least squares estimate is derived. With a proper choice of the regularization parameter, it is shown that the resulting estimate is root n consistent and possesses an oracle property; namely, it works as well as if the correct submodel were known. An automatic and data-driven approach was proposed to select the regularization parameter. Examples are used to illustrate the effectiveness of the newly proposed approach. The computer codes (written in MATLAB) to perform all calculation are available through the authors for an automatic data-driven variable selection procedure.
Motivated by recent work studying massive imaging data in the neuroimaging literature, we propose multivariate varying coefficient models (MVCM) for modeling the relation between multiple functional responses and a set of covariates. We develop several statistical inference procedures for MVCM and systematically study their theoretical properties. We first establish the weak convergence of the local linear estimate of coefficient functions, as well as its asymptotic bias and variance, and then we derive asymptotic bias and mean integrated squared error of smoothed individual functions and their uniform convergence rate. We establish the uniform convergence rate of the estimated covariance function of the individual functions and its associated eigenvalue and eigenfunctions. We propose a global test for linear hypotheses of varying coefficient functions, and derive its asymptotic distribution under the null hypothesis. We also propose a simultaneous confidence band for each individual effect curve. We conduct Monte Carlo simulation to examine the finite-sample performance of the proposed procedures. We apply MVCM to investigate the development of white matter diffusivities along the genu tract of the corpus callosum in a clinical study of neurodevelopment.
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