Explosive growth in computing power has made Bayesian methods for infinite-dimensional models - Bayesian nonparametrics - a nearly universal framework for inference, finding practical use in numerous subject areas. Written by leading researchers, this authoritative text draws on theoretical...

In survival data analysis, the proportional hazards (PH), accelerated failure time(AFT), and proportional odds (PO) models are commonly used semiparametric models forthe comparison of survivability in subjects. These models assume that the survival curvesdo not cross. However, in some clinical...

We consider the problem of estimating a sparse precision matrix of a multivariate Gaussian distribution, where the dimension p may be large. Gaussian graphical models provide an important tool in describing conditional independence through presence or absence of edges in the underlying graph. A...

Variable selection techniques for the classical linear regression model have been widely investigated. Variable selection in fully nonparametric and additive regression models has been studied more recently. A Bayesian approach for nonparametric additive regression models is considered, where...

In longitudinal studies, data are often missing despite every attempt made to collect complete data. When the missingness is informative and hence not ignorable, it is generally difficult to analyze non-ignorable missing (NIM) data since the distributional assumptions about missing data are not...

We show that rate-adaptive multivariate density estimation can be performed using Bayesian methods based on Dirichlet mixtures of normal kernels with a prior distribution on the kernel's covariance matrix parameter. We derive sufficient conditions on the prior specification that guarantee...