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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Resampling methods for model fitting and model selection.

G Jogesh Babu1

  • 1Department of Statistics, Pennsylvania State University, University Park, Pennsylvania 16802-2111, USA. babu@psu.edu

Journal of Biopharmaceutical Statistics
|October 26, 2011
PubMed
Summary
This summary is machine-generated.

This study explores resampling methods for statistical model fitting and selection. It highlights bootstrap methods for estimating distributions and a jackknife procedure for bias reduction in model selection.

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Area of Science:

  • Statistics
  • Computational Statistics

Background:

  • Nonparametric goodness-of-fit statistics often rely on the empirical distribution function.
  • Distribution-free properties of these statistics can be compromised in multivariate cases or when parameters are estimated.

Purpose of the Study:

  • To investigate resampling procedures for statistical model fitting and selection.
  • To address limitations of traditional goodness-of-fit statistics in complex scenarios.
  • To explore bias reduction techniques for model selection.

Main Methods:

  • Discussion of bootstrap methods for estimating underlying distributions.
  • Application of bootstrap methods for inference with unknown or partially specified distributions.
  • Introduction of a jackknife-type procedure for bias reduction in model selection.

Main Results:

  • Bootstrap methods effectively estimate distributions when traditional methods fail.
  • The proposed jackknife procedure focuses on bias reduction rather than bias estimation for model selection.
  • The findings are applicable to both one-dimensional and vector parameter spaces.

Conclusions:

  • Resampling techniques, particularly bootstrap, offer robust solutions for model fitting and selection.
  • Bias reduction via jackknife methods provides an alternative to bias estimation in model selection.
  • The study enhances statistical inference capabilities in complex data scenarios.