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 …

We review statistical methods for high-dimensional data analysis and pay particular
attention to recent developments for assessing uncertainties in terms of controlling false …

We introduce a new text-mining methodology that extracts sentiment information from news
articles to predict asset returns. Unlike more common sentiment scores used for stock return …

A stacked ensemble model is developed for forecasting and analyzing the daily average
concentrations of fine particulate matter (PM 2.5) in Beijing, China. Special feature extraction …

Least squares fitting is in general not useful for high-dimensional linear models, in which the
number of predictors is of the same or even larger order of magnitude than the number of …

In modern high-throughput data analysis, researchers perform a large number of statistical
tests, expecting to find perhaps a small fraction of significant effects against a predominantly …

We develop a framework for post model selection inference, via marginal screening, in
linear regression. At the core of this framework is a result that characterizes the exact …

Compressed sensing is an alternative to Shannon/Nyquist sampling for acquiring sparse or
compressible signals. Sparse coding represents a signal as a sparse linear combination of …

This paper studies the ordered weighted L1 (OWL) family of regularizers for sparse linear
regression with strongly correlated covariates. We prove sufficient conditions for clustering …

Large-scale data are often characterized by some degree of inhomogeneity as data are
either recorded in different time regimes or taken from multiple sources. We look at …