Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026

Same author

Low cardiac index during periods of arterial hypotension and risk of acute kidney injury in cardiac surgery.

British journal of anaesthesia·2026

Same author

Association of postoperative delirium with haemodynamic determinants of cerebral perfusion pressure during cardiac surgery: a retrospective cohort study.

British journal of anaesthesia·2026

Same author

Exploring Links between Brain Image-Derived Phenotypes and Accelerometer-Measured Physical Activity in the UK Biobank.

bioRxiv : the preprint server for biology·2026

Same author

Fast Bayesian Functional Principal Components Analysis.

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America·2026

Same author

Superpixel-ComBat modeling: A joint approach for harmonization and characterization of inter-scanner variability in T1-weighted images.

Imaging neuroscience (Cambridge, Mass.)·2025

Same journal

Towards optimal environmental policies: policy learning under arbitrary bipartite network interference.

Biostatistics (Oxford, England)·2026

Same journal

Multilevel functional quantile principal component analysis.

Biostatistics (Oxford, England)·2026

Same journal

Adaptive transfer learning for time-to-event modeling with applications in disease risk assessment.

Biostatistics (Oxford, England)·2026

Same journal

High-dimensional test for one-sided hypotheses.

Biostatistics (Oxford, England)·2026

Same journal

NBSR: a Negative Binomial Softmax Regression model for microRNA-seq data analysis.

Biostatistics (Oxford, England)·2026

Same journal

Addressing the influence of unmeasured confounding in observational studies with time-to-event outcomes: a semiparametric sensitivity analysis approach.

Biostatistics (Oxford, England)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Feb 4, 2026

Identification of Fatty Acids in Bacillus cereus

Identification of Fatty Acids in Bacillus cereus

Published on: December 5, 2016

The upstrap.

Ciprian M Crainiceanu¹, Adina Crainiceanu²

¹Department of Biostatistics, Johns Hopkins University, 615 N. Wolfe St. Baltimore, MD, USA.

Biostatistics (Oxford, England)

|September 26, 2018

Summary

This summary is machine-generated.

The upstrap is a novel resampling method that modifies the bootstrap by sampling with replacement more or fewer data points than the original sample size. This technique effectively addresses complex sample size calculation challenges.

Keywords:

Bootstrap sample size calculation sampling

More Related Videos

Glycomics-Guided Glycoproteomics Facilitates Comprehensive Profiling of the Glycoproteome in Complex Tumor Microenvironments

Glycomics-Guided Glycoproteomics Facilitates Comprehensive Profiling of the Glycoproteome in Complex Tumor Microenvironments

Published on: February 7, 2025

Multiplexed Fluorescent Immunohistochemical Staining, Imaging, and Analysis in Histological Samples of Lymphoma

Multiplexed Fluorescent Immunohistochemical Staining, Imaging, and Analysis in Histological Samples of Lymphoma

Published on: January 9, 2019

Related Experiment Videos

Last Updated: Feb 4, 2026

Identification of Fatty Acids in Bacillus cereus

Identification of Fatty Acids in Bacillus cereus

Published on: December 5, 2016

Glycomics-Guided Glycoproteomics Facilitates Comprehensive Profiling of the Glycoproteome in Complex Tumor Microenvironments

Glycomics-Guided Glycoproteomics Facilitates Comprehensive Profiling of the Glycoproteome in Complex Tumor Microenvironments

Published on: February 7, 2025

Multiplexed Fluorescent Immunohistochemical Staining, Imaging, and Analysis in Histological Samples of Lymphoma

Multiplexed Fluorescent Immunohistochemical Staining, Imaging, and Analysis in Histological Samples of Lymphoma

Published on: January 9, 2019

Area of Science:

Statistics
Computational Statistics

Background:

The bootstrap method is a foundational resampling technique for statistical inference, widely used for quantifying variability.
It involves sampling with replacement, using a sample size equal to the original dataset.

Purpose of the Study:

To introduce the upstrap, a novel resampling method that extends the bootstrap.
To demonstrate the utility of the upstrap in solving challenging sample size determination problems.

Main Methods:

The upstrap method is proposed, which involves sampling with replacement using a sample size that can be larger or smaller than the original data size.
The method's application is illustrated through a complex sample size calculation problem.

Main Results:

The upstrap provides a flexible approach to resampling.
The method is shown to be effective in addressing difficult sample size calculation scenarios.

Conclusions:

The upstrap is a valuable extension of the bootstrap method.
This technique offers a practical solution for complex statistical problems, particularly in sample size determination.