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NON‐PARAMETRIC BAYESIAN APPROACH TO HAZARD REGRESSION: A CASE STUDY WITH A LARGE NUMBER OF MISSING COVARIATE VALUES
Author(s) -
ARJAS ELJA,
LIU LIPING
Publication year - 1996
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(19960830)15:16<1757::aid-sim336>3.0.co;2-j
Subject(s) - covariate , bayesian probability , statistics , missing data , parametric statistics , proportional hazards model , multiplicative function , approximate bayesian computation , hazard , regression analysis , mathematics , computer science , econometrics , artificial intelligence , mathematical analysis , chemistry , organic chemistry , inference
A ‘packaged’ non‐parametric multiplicative hazard regression model is proposed, and applied to a study of the effects of some genetic and viral factors in the development of spontaneous leukaemia in mice. Hierarchical modelling and data augmentation are used to deal with the large number of missing covariate values. A Bayesian procedure is adopted, and the Metropolis–Hastings algorithm is used in the numerical computation of the posterior distribution.