In this paper, we consider the problem of computing a Wasserstein barycenter for a set of discrete probability distributions with finite supports, wh…

The recent paucity of sunspots and the delay in the expected start of Solar Cycle 24 have drawn attention to the challenges involved in predicting so…

The discrete distribution clustering algorithm, namely D2-clustering, has demonstrated its usefulness in image classification and annotation where ea…

The research questions that motivate transportation safety studies are causal in nature. Safety researchers typically use observational data to answe…

For the nonparametric estimation of multivariate finite mixture models with the conditional independence assumption, we propose a new formulation of …

We introduce a new sufficient dimension reduction framework that targets a statistical functional of interest, and propose an efficient estimator for…

Sz\'{e}kely, Rizzo and Bakirov (Ann. Statist. 35 (2007) 2769-2794) and Sz\'{e}kely and Rizzo (Ann. Appl. Statist. 3 (2009) 1236-1265), in two seminal…

How will the climate system respond to anthropogenic forcings? One approach to this question relies on climate model projections. Current climate pro…

Interactions among multiple genes across the genome may contribute to the risks of many complex human diseases. Whole-genome single nucleotide polymo…

Markov chain Monte Carlo (MCMC) algorithms provide a very general recipe for estimating properties of complicated distributions. While their use has become commonplace and there is a large literature on MCMC theory and practice, MCMC users still hav…

Markov chain Monte Carlo (MCMC) algorithms provide a very general recipe for estimating properties of complicated distributions. While their use has …

We investigate asymptotic properties of least-absolute-deviation or median quantile estimates of the location and scale functions in nonparametric re…

In preprocessing tensor-valued data, e.g. images and videos, a common procedure is to vectorize the observations and subject the resulting vectors to…

We study maximum likelihood estimation for the statistical model for undirected random graphs, known as the $\beta$-model, in which the degree sequen…

This paper is concerned with the selection and estimation of fixed and random effects in linear mixed effects models. We propose a class of nonconcav…

We consider the problem of calculating distance correlation coefficients between random vectors whose joint distributions belong to the class of Lanc…

We study statistical inferences for a class of modulated stationary processes with time-dependent variances. Due to non-stationarity and the large nu…

Motivated by recent work studying massive imaging data in the neuroimaging literature, we propose multivariate varying coefficient models (MVCM) for …

We investigate a Gaussian mixture model (GMM) with component means constrained in a pre-selected subspace. Applications to classification and cluster…

We study parameter estimation in linear Gaussian covariance models, which are $p$-dimensional Gaussian models with linear constraints on the covarian…

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