KCYP data analysis using Bayesian multivariate linear model

A Bayesian Multivariate Functional Dynamic Linear Model

We present a Bayesian approach for modeling multivariate, dependent functional data. To account for the three dominant structural features in the data—functional, time dependent, and multivariate components—we extend hierarchical dynamic linear models for multivariate time series to the functional data setting. We also develop Bayesian spline theory in a more general constrained optimization fr...

Bayesian Robust Multivariate Linear Regression With Incomplete Data

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Bayesian Prediction in Spatial Data Analysis

Bayesian model determination for multivariate ordinal and binary data

We consider how to compare different conditional independence specifications for ordinal categorical data, by calculating a posterior distribution over classes of graphical models. The approach is based on the multivariate ordinal probit model (Chib and Greenberg, 1998) where the data are considered to have arisen as truncated multivariate normal random vectors. By parameterising the precision ...

Biological monitoring: A Bayesian Model for Multivariate Compositional Data

We develop a model to relate a multivariate compositional response to a number of covariates. We propose a new graphical model, called the Random Effects Discrete Regression (REDR) model, which allows for examination of the complex conditional relationships between a set of covariates and multiple discrete response variables. Our approach offers a number of advantages over previous approaches a...