Nowadays, the growth of the high-throughput technologies has resulted in exponential
growth in the harvested data with respect to both dimensionality and sample size. The trend …

Big Data bring new opportunities to modern society and challenges to data scientists. On the
one hand, Big Data hold great promises for discovering subtle population patterns and …

On asymptotically optimal confidence regions and tests for high-dimensional models Page 1
The Annals of Statistics 2014, Vol. 42, No. 3, 1166–1202 DOI: 10.1214/14-AOS1221 © Institute …

Fitting high-dimensional statistical models often requires the use of non-linear parameter
estimation procedures. As a consequence, it is generally impossible to obtain an exact …

We propose robust methods for inference about the effect of a treatment variable on a scalar
outcome in the presence of very many regressors in a model with possibly non-Gaussian …

The purpose of this paper is to propose methodologies for statistical inference of low
dimensional parameters with high dimensional data. We focus on constructing confidence …

Nowadays, with a high dimensionality of dataset, it faces a great challenge in the creation of
effective methods which can select an optimal variables subset. In this study, a strategy that …

The theory and practice of statistical methods in situations where the available data are
functions (instead of real numbers or vectors) is often referred to as Functional Data Analysis …

We introduce a new estimator, the simultaneous multiscale change point estimator SMUCE,
for the change point problem in exponential family regression. An unknown step function is …

Abstract We consider the Principal Component Analysis problem for large tensors of
arbitrary order k under a single-spike (or rank-one plus noise) model. On the one hand, we …