Semi-parametric Bayesian inference for multi-season baseball data

Bayesian Nonparametric and Parametric Inference

This paper reviews Bayesian Nonparametric methods and discusses how parametric predictive densities can be constructed using nonparametric ideas.

Semi-parametric Inference for Copula Models for Truncated Data

We investigate the dependent relationship between two failure time variables that truncate each other. Chaieb, Rivest, and Abdous (2006) proposed a semiparametric model under the so-called “semi-survival” Archimedean-copula assumption and discussed estimation of the association parameter, the truncation probability, and the marginal functions. Here the same model assumption is adopted but diffe...

Semi-parametric Inference for Copula Models for Dependently Truncated Data

Applied Bayesian Non- and Semi-parametric Inference using DPpackage

Inmany practical situations, a parametric model cannot be expected to describe in an appropriate manner the chance mechanism generating an observed dataset, and unrealistic features of some common models could lead to unsatisfactory inferences. In these cases, we would like to relax parametric assumptions to allow greater modeling flexibility and robustness against misspecification of a paramet...

Bayesian inference for longitudinal data with non-parametric treatment effects.

We consider inference for longitudinal data based on mixed-effects models with a non-parametric Bayesian prior on the treatment effect. The proposed non-parametric Bayesian prior is a random partition model with a regression on patient-specific covariates. The main feature and motivation for the proposed model is the use of covariates with a mix of different data formats and possibly high-order...