Dependent generalized Dirichlet process priors for the analysis of acute lymphoblastic leukemia

William Barcella, Maria De Iorio, Stefano Favaro, Gary L. Rosner

Research output: Contribution to journalArticlepeer-review

Abstract

We propose a novel Bayesian nonparametric process prior for modeling a collection of random discrete distributions. This process is defined by including a suitable Beta regression framework within a generalized Dirichlet process to induce dependence among the discrete random distributions. This strategy allows for covariate dependent clustering of the observations. Some advantages of the proposed approach include wide applicability, ease of interpretation, and availability of efficient MCMC algorithms. The motivation for this work is the study of the impact of asparginage metabolism on lipid levels in a group of pediatric patients treated for acute lymphoblastic leukemia.

Original languageEnglish (US)
Pages (from-to)342-358
Number of pages17
JournalBiostatistics
Volume19
Issue number3
DOIs
StatePublished - Jul 1 2018

Keywords

  • Bayesian nonparametrics
  • Beta regression
  • Dependent random probability measures
  • Generalized Dirichlet process
  • Stick-breaking processes

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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