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Bootstrapping a change-point Cox model for survival data.

Gongjun Xu1, Bodhisattva Sen2, Zhiliang Ying2

  • 1School of Statistics, University of Minnesota.

Electronic Journal of Statistics
|November 18, 2014
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Summary
This summary is machine-generated.

This study examines bootstrap methods for change-point detection in Cox models with censored survival data. A new model-based bootstrap is consistent, unlike the standard nonparametric approach, improving inference accuracy.

Keywords:
(in)-consistency of bootstrapChange-point in timem-out-of-n bootstrapnon-standard asymptoticssmoothed bootstrap

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Area of Science:

  • Survival analysis
  • Statistical inference
  • Time series analysis

Background:

  • Change-point detection is crucial in survival analysis.
  • The Cox proportional hazards model is widely used for time-to-event data.
  • Inference for change-points in survival models presents statistical challenges.

Purpose of the Study:

  • To investigate the consistency of bootstrap methods for change-point inference in the Cox model.
  • To establish a criterion for bootstrap method consistency.
  • To propose and validate a new, consistent bootstrap approach.

Main Methods:

  • Theoretical analysis of bootstrap consistency.
  • Development of a novel model-based bootstrap procedure.
  • Simulation studies to compare bootstrap schemes.

Main Results:

  • A general criterion for bootstrap consistency is established.
  • The standard nonparametric bootstrap is found to be inconsistent.
  • The proposed model-based bootstrap method is proven to be consistent.

Conclusions:

  • The standard nonparametric bootstrap is unreliable for change-point estimation in this context.
  • The novel model-based bootstrap offers a consistent and reliable alternative.
  • Simulation results support the theoretical findings and demonstrate practical utility.