With the advance of modern technology, more and more data are being recorded
continuously during a time interval or intermittently at several discrete time points. These are …

Standard regression approaches assume that some finite number of the response
distribution characteristics, such as location and scale, change as a (parametric or …

Functional data clustering is to identify heterogeneous morphological patterns in the
continuous functions underlying the discrete measurements/observations. Application of …

Gaussian Process Regression Analysis for Functional Data presents nonparametric
statistical methods for functional regression analysis, specifically the methods based on a …

This paper studies convergence behavior of latent mixing measures that arise in finite and
infinite mixture models, using transportation distances (ie, Wasserstein metrics). The …

Functional data analysis (FDA) is a relatively new branch in statistics. Experiments where a
complete function is observed for each individual give rise to functional data. In this work we …

Spatial analysis has grown at a remarkable rate over the past two decades. Fueled by
sophisticated GIS software and inexpensive and fast computation, collection of data with …

This chapter provides a brief review and motivation for the use of nonparametric Bayes
methods in biostatistical applications. Clearly, the nonparametric Bayes biostatistical …

Flexible covariate-dependent density estimation can be achieved by modelling the joint
density of the response and covariates as a Dirichlet process mixture. An appealing aspect …

A logistic stick-breaking process (LSBP) is proposed for non-parametric clustering of general
spatially-or temporally-dependent data, imposing the belief that proximate data are more …