Accession Number:

ADA162443

Title:

Bootstrapping Cox's Regression Model.

Descriptive Note:

Technical rept.,

Corporate Author:

STANFORD UNIV CA LAB FOR COMPUTATIONAL STATISTICS

Personal Author(s):

Report Date:

1985-11-01

Pagination or Media Count:

61.0

Abstract:

Statistical inference in Coxs 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

Subject Categories:

  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE