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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...
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An R-Based Landscape Validation of a Competing Risk Model
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Monte Carlo studies of bootstrap variability in ROC analysis with data dependency.

Jin Chu Wu1, Alvin F Martin1, Raghu N Kacker1

  • 1National Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA.

Communications in Statistics: Simulation and Computation
|March 14, 2020
PubMed
Summary

This study addresses data dependency in Receiver Operating Characteristic (ROC) analysis using a two-layer bootstrap method. It recommends 2,000 bootstrap replications for accurate statistical analysis when dealing with limited, dependent data.

Keywords:
Bootstrap replicationsBootstrap variabilityData dependencyLarge datasetsROC analysisStandard error

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Area of Science:

  • Statistics
  • Biostatistics
  • Machine Learning

Background:

  • Receiver Operating Characteristic (ROC) analysis is crucial for classifier performance evaluation across disciplines.
  • Data dependency, arising from repeated subject use, is common due to resource limitations.
  • Estimating standard errors for ROC statistics with dependent data requires specialized methods.

Purpose of the Study:

  • To determine the optimal number of bootstrap replications for ROC analysis with data dependency.
  • To reduce bootstrap variance and enhance computational accuracy.
  • To provide guidance for robust statistical inference in ROC analysis.

Main Methods:

  • A two-layer data structure was employed to handle data dependency.
  • Nonparametric two-sample two-layer bootstrap was utilized for standard error estimation.
  • Monte Carlo studies assessed bootstrap variability to find appropriate replication numbers.

Main Results:

  • The study investigated bootstrap variability in ROC analysis with dependent data.
  • A specific number of bootstrap replications was identified as appropriate.
  • A tolerance of 0.02 for the coefficient of variation was used.

Conclusions:

  • 2,000 bootstrap replications are suggested for ROC analysis with data dependency.
  • This recommendation ensures accuracy and reduces variance in statistical computations.
  • The findings support reliable classifier evaluation in resource-constrained scenarios.