Dependent generalized Dirichlet process priors for the analysis of acute lymphoblastic leukemia
| Autor: | Maria De Iorio, William Barcella, Stefano Favaro, Gary L. Rosner |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Statistics and Probability Computer science Lymphoblastic Leukemia Antineoplastic Agents Biostatistics 01 natural sciences Statistics Nonparametric Bayesian nonparametrics 010104 statistics & probability 03 medical and health sciences Outcome Assessment Health Care Prior probability Covariate Beta regression Dependent random probability measures Asparaginase Humans Applied mathematics 0101 mathematics Child Cluster analysis Mcmc algorithm Generalized Dirichlet process Stick-breaking processes Statistics Probability and Uncertainty Models Statistical Statistics Bayes Theorem Articles General Medicine Precursor Cell Lymphoblastic Leukemia-Lymphoma Dirichlet process 030104 developmental biology Data Interpretation Statistical Probability and Uncertainty |
| Popis: | SUMMARY 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. |
| Databáze: | OpenAIRE |
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