We consider Frechet sufficient dimension reduction with responses being complex random objects in a metric space and high-dimensional Euclidean predic......
Sequential Monte Carlo algorithms are widely accepted as powerful computational tools for making inference with dynamical systems. A key step in seque......
We consider forecasting a single time series using a large number of predictors in the presence of a possible nonlinear forecast function. Assuming th......
A problem of major interest in network data analysis is to explain the strength of connections using context information. To achieve this, we introduc......
This paper is concerned with the problem of comparing the population means of two groups of independent observations. An approximate randomization tes......
Post-selection inference on thousands of parameters has attracted considerable research interest in recent years. Specifically, Benjamini & Yekuti......
In this paper we investigate a divide-and-conquer algorithm for estimating the extreme value index when data are stored in multiple machines. The orac......
The classical integrated conditional moment test is a promising method for model checking and its basic idea has been applied to develop several varia......
We propose a new method for functional nonparametric regression with a predictor that resides on a finite-dimensional manifold, but is observable only......
Statistical analysis on networks has received growing attention due to demand from various emerging applications. In dynamic networks, one of the key ......
Experimental designs that spread points apart from each other on projections are important for computer experiments, when not necessarily all factors ......
Progression of chronic disease is often manifested by repeated occurrences of disease-related events over time. Delineating the heterogeneity in the r......
We consider testing the covariance structure in statistical models. We focus on developing such tests when the random vectors of interest are not dire......
High-dimensional statistical inference with general estimating equations is challenging and remains little explored. We study two problems in the area......
Models for analysing multivariate datasets with missing values require strong, often unassessable, assumptions. The most common of these is that the m......
