Bootstrapping
Bootstrapping - Related Videos
01:24
Bootstrapping
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2025
The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is small or...
07:35
How Virtual Celebrity Characteristics Drive Purchase Intention: Testing the Stimulus-Organism-Response Framework with Structural Equation Modeling
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2026
In the context of the virtual celebrity experience, content quality most strongly drives satisfaction; satisfaction, in turn, increases purchase intention indirectly via loyalty. Personalization strengthens the satisfaction-loyalty link, so firms should prioritize emotion-evoking, narrative-consistent virtual celebrity content and personalized loyalty programs.
10:07
Thermal Imaging to Study Stress Non-invasively in Unrestrained Birds
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Cited by 55 •
2015
There is a need for a non-invasive assessment of stress. This paper describes a simple protocol using thermal imaging to detect a significant response in eye-region temperature in wild blue tits to a mild acute stressor.
10:11
Portable Intermodal Preferential Looking (IPL): Investigating Language Comprehension in Typically Developing Toddlers and Young Children with Autism
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Cited by 32 •
2012
A reliable home-based way to assess the language comprehension of very young typically developing children, as well as those with autism, is described. The method analyzes children's eye gaze while viewing side-by-side images but hearing an audio that matches only one image. Stimuli are designed with young participants in mind.
14:27
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Cited by 73 •
2013
Multivariate techniques including principal component analysis (PCA) have been used to identify signature patterns of regional change in functional brain images. We have developed an algorithm to identify reproducible network biomarkers for the diagnosis of neurodegenerative disorders, assessment of disease progression, and objective evaluation of treatment effects in patient populations.
FAQs
Sampling with replacement allows each resample to differ from the observed dataset while retaining the dataset as the source of information. Repeating the calculation across these resamples produces a sampling distribution for the chosen statistic. The spread and shape of that distribution provide evidence about how variable the sample-based result may be.
Bootstrapping estimates uncertainty directly from repeated resampling rather than depending on strong assumptions about the population distribution or on an available analytical formula. This distinction matters when the statistic is complex, the sample is small, or the data are not normally distributed. It can therefore extend uncertainty analysis to situations where conventional calculations are difficult.
The resulting sampling distribution can support estimates of standard errors, confidence intervals, and bias. Standard errors describe the estimated uncertainty around a statistic, while confidence intervals express a range based on the resampling results. Examining bias helps researchers assess whether the sample-based estimate systematically differs from the values suggested by the resampled analyses.
First, begin with the observed dataset and repeatedly draw new samples with replacement. Next, calculate the selected statistic for every resample, such as a mean, median, regression coefficient, or correlation. Finally, use the collection of calculated values as a sampling distribution to estimate uncertainty, reliability, standard errors, confidence intervals, or bias.
The approach can be applied to means, medians, regression coefficients, and correlations, rather than being limited to a single type of summary. For each statistic, repeated resampling produces a corresponding set of estimates. Researchers can then evaluate the statistic’s uncertainty or reliability, including cases where a direct analytical calculation is difficult or unavailable.
Bootstrapping is particularly useful when researchers need to quantify uncertainty for complex statistics, small samples, or data that do not follow a normal distribution. Its applications span experimental, biomedical, social, and computational studies. In each setting, the method helps characterize the reliability of sample-based findings when conventional analytical formulas may be difficult to use.