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

Sufficient dimension reduction is a rapidly develo** research field that has wide
applications in regression diagnostics, data visualization, machine learning, genomics …

Handbook of Regression Methods concisely covers numerous traditional, contemporary,
and nonstandard regression methods. The handbook provides a broad overview of …

High-dimensional data have frequently been collected in many scientific areas including
genomewide association study, biomedical imaging, tomography, tumor classifications, and …

Data-analytic approaches to regression problems, arising from many scientific disciplines
are described in this book. The aim of these nonparametric methods is to relax assumptions …

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 …

Semiparametric regression is concerned with the flexible incorporation of non-linear
functional relationships in regression analyses. Any application area that benefits from …

The existence of high speed, inexpensive computing has made it easy to look at data in
ways that were once impossible. Where once a data analyst was forced to make restrictive …

Since our first edition of this book, many developments in statistical mod elling based on
generalized linear models have been published, and our primary aim is to bring the book up …

Amongmanyexcitingdevelopmentsinstatistic…, nonlineartimeseriesanddata-
analyticnonparametricmethodshavegreatly advanced along seemingly unrelated paths. In …