Bayesian Nonparametric Modeling for Multivariate Ordinal Regression

Bayesian Nonparametric Modeling for Multivariate Ordinal Regression

Univariate or multivariate ordinal responses are often assumed to arise from a latent continuous parametric distribution, with covariate effects which enter linearly. We introduce a Bayesian nonparametric modeling approach for univariate and multivariate ordinal regression, which is based on mixture modeling for the joint distribution of latent responses and covariates. The modeling framework e...

Bayesian Nonparametric Modeling in Quantile Regression

We propose Bayesian nonparametric methodology for quantile regression modeling. In particular, we develop Dirichlet process mixture models for the error distribution in an additive quantile regression formulation. The proposed nonparametric prior probability models allow the data to drive the shape of the error density and thus provide more reliable predictive inference than models based on par...

Multivariate Nonparametric Regression

As in many areas of biostatistics, oncological problems often have multivariate predictors. While assuming a linear additive model is convenient and straightforward, it is often not satisfactory when the relation between the outcome measure and the predictors is either nonlinear or nonadditive. In addition, when the number of predictors becomes (much) larger than the number of independent obser...

Logistic Regression for Multivariate Longitudinal Ordinal Data

Bayesian Nonparametric Regression Modeling of Panel Data for Sequential Classification.

This paper proposes a Bayesian nonparametric regression model of panel data for sequential pattern classification. The proposed method provides a flexible and parsimonious model that allows both time-independent spatial variables and time-dependent exogenous variables to be predictors. Not only this method improves the accuracy of parameter estimation for limited data, but also it facilitates m...