Statistical Foundations of Data Science gives a thorough introduction to commonly used
statistical models, contemporary statistical machine learning techniques and algorithms …

This paper introduces a Projected Principal Component Analysis (Projected-PCA), which
employees principal component analysis to the projected (smoothed) data matrix onto a …

This paper discusses statistical methods for estimating complex correlation structure from
large pharmacogenomic datasets. We selectively review several prominent statistical …

We consider large-scale studies in which thousands of significance tests are performed
simultaneously. In some of these studies, the multiple testing procedure can be severely …

High dimensional data suffer from unwanted variation, such as the batch effects common in
microarray data. Unwanted variation complicates the analysis of high dimensional data …

Heavy-tailed errors impair the accuracy of the least squares estimate, which can be spoiled
by a single grossly outlying observation. As argued in the seminal work of Peter Huber in …

Large-scale multiple testing with correlated test statistics arises frequently in much scientific
research. Incorporating correlation information in approximating the false discovery …

Factor models are a class of powerful statistical models that have been widely used to deal
with dependent measurements that arise frequently from various applications from genomics …

Large-scale multiple testing with correlated and heavy-tailed data arises in a wide range of
research areas from genomics, medical imaging to finance. Conventional methods for …

Modeling responses on the nodes of a large-scale network is an important task that arises
commonly in practice. This paper proposes a community network vector autoregressive …