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A semiparametric Bayesian model for repeatedly repeated binary outcomes
(WILEY-BLACKWELL, 2008)
We discuss the analysis of data from single-nucleotide polymorphism arrays comparing tumour and normal tissues. The data consist of sequences of indicators for loss of heterozygosity (LOH) and involve three nested levels ...
Semi-parametric bayesian inference for multi-season baseball data
(INT SOC BAYESIAN ANALYSIS, 2008)
A bayesian semiparametric temporally-stratified proportional hazards model with spatial frailties
(INT SOC BAYESIAN ANALYSIS, 2012)
Bayesian analysis of survival data with missing censoring indicators
(WILEY, 2021)
In some large clinical studies, it may be impractical to perform the physical examination to every subject at his/her last monitoring time in order to diagnose the occurrence of the event of interest. This gives rise to ...
A bayesian semiparametric temporally-stratified proportional hazards model with spatial frailtiesBAYESIAN ANALYSISBAYESIAN ANAL
(INT SOC BAYESIAN ANALYSIS, 2016)
Semi-parametric Bayesian Inference for Multi-Season Baseball Data
(INT SOC BAYESIAN ANALYSIS, 2008)
We analyze complete sequences of successes (hits, walks, and sacrifices) for a group of players from the American and National Leagues, collected over 4 seasons. The goal is to describe how players' performance vary from ...
Semi-parametric bayesian inference for multi-season baseball dataBAYESIAN ANALYSIS (ONLINE)
(INT SOC BAYESIAN ANALYSIS, 2016)
BAYESIAN SEMIPARAMETRIC INFERENCE FOR MULTIVARIATE DOUBLY-INTERVAL-CENSORED DATA
(INST MATHEMATICAL STATISTICS, 2010)
Based on a data set obtained in a dental longitudinal study, conducted in Flanders (Belgium), the joint time to caries distribution of permanent first molars was modeled as a function of covariates. This involves an analysis ...
DPpackage: Bayesian Semi- and Nonparametric Modeling in R
(JOURNAL STATISTICAL SOFTWARE, 2011)
Data analysis sometimes requires the relaxation of parametric assumptions in order to gain modeling flexibility and robustness against mis-specification of the probability model. In the Bayesian context, this is accomplished ...