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 …

The limitations of the well‐known LASSO regression as a variable selector are tested when
there exists dependence structures among covariates. We analyse both the classic situation …

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 …

Big data (BD) has attracted increasing attention from both academics and practitioners. This
paper aims at illustrating the role of big data analytics in supporting world-class sustainable …

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 …

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

Ordinary least squares and stepwise selection are widespread in behavioral science
research; however, these methods are well known to encounter overfitting problems such …

We propose a new method for estimation in linear models. The 'lasso'minimizes the residual
sum of squares subject to the sum of the absolute value of the coefficients being less than a …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

Praise for the first edition:"[This book] succeeds singularly at providing a structured
introduction to this active field of research.… it is arguably the most accessible overview yet …