Many complex dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes. We conside…

We develop a framework and methodology for causal inference with linked data. In particular, we describe conceptually how errors in linking can impac…

In a variety of application areas, there is a growing interest in analyzing high dimensional sparse count data, with sparsity exhibited by an over-ab…

In contingency table analysis, sparse data is frequently encountered for even modest numbers of variables, resulting in non-existence of maximum like…

We present a Bayesian model for estimating the joint distribution of multivariate categorical data when units are nested within groups. Such data ari…

This paper develops methodology for local sensitivity analysis based on directional derivatives associated with spatial processes. Formal gradient an…

Technological advances in genotyping have given rise to hypothesis-based association studies of increasing scope. As a result, the scientific hypothe…

Bayesian sparse factor models have proven useful for characterizing dependence, but scaling computation to high dimensions is problematic. We propose…

Detecting boundary of an image based on noisy observations is a fundamental problem of image processing and image segmentation. For a $d$-dimensional…

Mixtures of Zellner's g-priors have been studied extensively in linear models and have been shown to have numerous desirable properties for Bayesian …

In this section we collect some auxiliary results about Gibbs posterior inference. We begin with a converse to Theorem 2 on the exponential scale: if U is an open set intersecting Θmin, then the Gibbs posterior measure of U cannot be exponentially s…

In this paper we consider a Bayesian framework for making inferences about dynamical systems from ergodic observations. The proposed Bayesian procedu…

Statistical inference based on moment conditions and estimating equations is of substantial interest when it is difficult to specify a full probabili…

In recent years, a rich variety of regularization procedures have been proposed for high dimensional regression problems. However, tuning parameter c…

This article proposes a unified framework, the balancing weights, for estimating causal effects with multi-valued treatments using propensity score w…

Most generative models for clustering implicitly assume that the number of data points in each cluster grows linearly with the total number of data p…

In order to use persistence diagrams as a true statistical tool, it would be very useful to have a good notion of mean and variance for a set of diag…

Item response theory (IRT) models have been widely used in educational measurement testing. When there are repeated observations available for indivi…

We develop a novel parallel resampling algorithm for fully parallelized particle filters, which is designed with GPUs (graphics processing units) or …

In nonparametric regression problems involving multiple predictors, there is typically interest in estimating an anisotropic multivariate regression …

Shape constrained regression analysis has applications in dose-response modeling, environmental risk assessment, disease screening and many other are…

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