Bayesian Analysis of Semiparametric Proportional Hazards Models

Abstract

We consider the usual proportional hazards model in the case where the baseline hazard, the covariate link and the covariate coefficients are all unknown. Both the baseline hazard and the covariate link are monotone functions and are characterized nonparametrically using a dense class arising as a mixture of Beta distribution functions. We take a Bayesian approach for fitting such a model. Since interest focuses more upon the likelihood, we consider vague prior specifications including Jeffreys's prior. Computations are carried out using sampling-based methods. Model criticism is also discussed. Finally, a data set studying survival of a sample of lung cancer patients is analyzed.

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Document Details

Document Type
Technical Report
Publication Date
Mar 21, 1994
Accession Number
ADA279394

Entities

People

  • Alan E. Gelfand
  • Bani K. Mallick

Organizations

  • Stanford University

Tags

Communities of Interest

  • Biomedical
  • C4I

DTIC Thesaurus Topics

  • Algorithms
  • Bayesian Inference
  • Bayesian Networks
  • Data Science
  • Data Sets
  • Distribution Functions
  • Estimators
  • Information Science
  • Lung Cancer
  • Models
  • Monotone Functions
  • Monte Carlo Method
  • Probability
  • Random Variables
  • Sampling
  • Security
  • Statistical Analysis

Fields of Study

  • Mathematics

Readers

  • Computational Modeling and Simulation
  • Statistical inference.

Technology Areas

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