Bayesian semiparametric modeling for stochastic precedence, with applications in epidemiology and survival analysis

被引：0
作者：
Athanasios Kottas
机构：
[1] University of California,Department of Applied Mathematics and Statistics, School of Engineering, MS: SOE2
来源：
Lifetime Data Analysis | 2011年 / 17卷
关键词：
Dirichlet process prior; Markov chain Monte Carlo; Mixtures of normal distributions; Receiver operating characteristic curve; Stochastic order; Survival function;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
We propose a prior probability model for two distributions that are ordered according to a stochastic precedence constraint, a weaker restriction than the more commonly utilized stochastic order constraint. The modeling approach is based on structured Dirichlet process mixtures of normal distributions. Full inference for functionals of the stochastic precedence constrained mixture distributions is obtained through a Markov chain Monte Carlo posterior simulation method. A motivating application involves study of the discriminatory ability of continuous diagnostic tests in epidemiologic research. Here, stochastic precedence provides a natural restriction for the distributions of test scores corresponding to the non-infected and infected groups. Inference under the model is illustrated with data from a diagnostic test for Johne’s disease in dairy cattle. We also apply the methodology to the comparison of survival distributions associated with two distinct conditions, and illustrate with analysis of data on survival time after bone marrow transplantation for treatment of leukemia.
引用
收藏
页码：135 / 155
页数：20
相关论文
共 54 条
  • [11] Dunson DB(1997)Bayesian analysis of stochastically ordered distributions of categorical variables J Am Stat Assoc 92 208-214
  • [12] Collins MT(1973)A Bayesian analysis of some nonparametric problems Ann Stat 1 209-230
  • [13] Wells SJ(1998)Model choice: a minimum posterior predictive loss approach Biometrika 85 1-11
  • [14] Petrini KR(2001)Nonparametric Bayesian modeling for stochastic order Ann Inst Stat Math 53 865-876
  • [15] Collins JE(2002)A computational approach for full nonparametric Bayesian inference under Dirichlet process mixture models J Comput Graph Stat 11 289-305
  • [16] Schultz RD(1991)Nonparametric Bayesian bioassay including ordered polytomous response Biometrika 78 657-666
  • [17] Whitlock RH(2008)Modeling stochastic order in the analysis of receiver operating characteristic data: Bayesian non-parametric approaches J R Stat Soc C (Appl Stat) 57 207-225
  • [18] Dunson DB(2003)Bayesian methods for partial stochastic orderings Biometrika 90 303-317
  • [19] Peddada SD(2007)Bayesian nonparametric inference of stochastically ordered distributions, with Pólya trees and Bernstein polynomials Stat Probab Lett 77 907-913
  • [20] Erkanli A(2006)Nonparametric Bayesian survival analysis using mixtures of Weibull distributions J Stat Plann Inference 136 578-596
123456