The Bernstein--von Mises theorem for the proportional hazard model

Abstract

We study large sample properties of Bayesian analysis of the proportional hazard model with neutral to the right process priors on the baseline hazard function. We show that the posterior distribution of the baseline cumulative hazard function and regression coefficients centered at the maximum likelihood estimator is jointly asymptotically equivalent to the sampling distribution of the maximum likelihood estimator.

Published at http://dx.doi.org/10.1214/009053606000000533 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (1)

• Finite sample properties of multiple imputation estimators

Cited by in corpus (6)

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• On the Bernstein-von Mises phenomenon for nonparametric Bayes procedures
• The semiparametric Bernstein-von Mises theorem
• Bernstein von Mises Theorems for Gaussian Regression with increasing number of regressors
• A Bernstein-von Mises theorem for smooth functionals in semiparametric models
• Finite Sample Bernstein -- von Mises Theorem for Semiparametric Problems