We first provide further results for the exploratory analysis, in particular regarding the spatial
variation of the SST seasonality profile across the Red Sea, the estimated EOFs, and the …

Bayesian semiparametric inference for multivariate doubly-interval-censored data Page 1 The
Annals of Applied Statistics 2010, Vol. 4, No. 4, 2126–2149 DOI: 10.1214/10-AOAS368 © …

As a generalization of the Dirichlet process (DP) to allow predictor dependence, we propose
a local Dirichlet process (lDP). The lDP provides a prior distribution for a collection of …

Spatial Product Partition Models Page 1 Bayesian Analysis (2016) 11, Number 1, pp. 265–298
Spatial Product Partition Models ∗ Garritt L. Page † and Fernando A. Quintana ‡ Abstract …

Dependent nonparametric processes extend distributions over measures, such as the
Dirichlet process and the beta process, to give distributions over collections of measures …

Multiple time series data may exhibit clustering over time and the clustering effect may
change across different series. This paper is motivated by the Bayesian non-parametric …

In analyzing data from multiple related studies, it often is of interest to borrow information
across studies and to cluster similar studies. Although parametric hierarchical models are …

Although most models for rainfall extremes focus on pointwise values, it is aggregated
precipitation over areas up to river catchment scale that is of the most interest. To capture the …

Latent variable models represent hidden structure in observational data. To account for the
distribution of the observational data changing over time, space or some other covariate, we …

Predicting the response at an unobserved location is a fundamental problem in spatial
statistics. Given the difficulty in modeling spatial dependence, especially in nonstationary …