Bootstrapping Cox's Regression Model.

Abstract

Statistical inference in Cox's regression model is usually carried out using traditional (first order) large sample theory. In the spirit of earlier success stories one might try to bootstrap data in order to better assess the sampling variability of the Cox estimator. Such a bootstrap scheme is proposed in this paper. An asymptotic justification is given, showing that inference based on the bootstrap procedure is first order equivalent to the standard one. The problem of constructing more accurate moderate-sample confidence intervals is also addressed, employing second order fine-tuning of the bootstrap. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1985
Accession Number
ADA162443

Entities

People

  • Nils L. Hjort

Organizations

  • Stanford University

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Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Computational Science
  • Data Science
  • Differential Equations
  • Equations
  • Information Science
  • Linear Accelerators
  • Military Research
  • New York
  • Probability
  • Probability Distributions
  • Random Variables
  • Statistical Algorithms
  • Statistical Analysis
  • Statistical Inference
  • Statistics
  • Stochastic Processes
  • Surveys

Fields of Study

  • Mathematics

Readers

  • Neural Network Machine Learning.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms