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Related Concept Videos

Bootstrapping01:24

Bootstrapping

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...
Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
Survival Tree01:19

Survival Tree

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Related Experiment Video

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

A comparison of bootstrap methods and an adjusted bootstrap approach for estimating the prediction error in

Wenyu Jiang1, Richard Simon

  • 1Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, 6130 Executive Boulevard, Rockville, MD 20852, USA. wjiang@mathstat.concordia.ca

Statistics in Medicine
|July 13, 2007
PubMed
Summary

This study reviews prediction error estimation methods for microarray data. A new adjusted bootstrap (ABS) method offers a robust and less biased approach, especially for small sample sizes in gene classification.

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Area of Science:

  • Bioinformatics
  • Statistical Learning
  • Genomics

Background:

  • Accurate prediction error estimation is crucial for microarray data classification.
  • Existing methods, particularly bootstrap-related ones, exhibit bias or variability with small sample sizes where the number of genes exceeds specimens.

Purpose of the Study:

  • To critically review existing prediction error estimation methods for high-dimensional microarray data.
  • To introduce and evaluate novel bootstrap-based methods addressing limitations of current techniques.

Main Methods:

  • A critical review of existing prediction error estimation methods, focusing on bootstrap techniques.
  • Introduction of a repeated leave-one-out bootstrap (RLOOB) method.
  • Proposal of an adjusted bootstrap (ABS) method utilizing a learning curve fitted to RLOOB estimates.

Main Results:

  • Existing methods show substantial bias or variability for small sample sizes in microarray classification.
  • The proposed RLOOB and ABS methods demonstrate improved robustness.
  • The ABS method provides a slightly conservative, yet reliable, prediction error estimate without significant bias or variability.

Conclusions:

  • The adjusted bootstrap (ABS) method is a robust and effective approach for estimating prediction error in microarray classification, particularly with small sample sizes.
  • ABS outperforms existing methods like leave-one-out bootstrap and 0.632+ bootstrap by mitigating bias and variability.