Chapman and Hall/Crc Monographs on Statistics and Applied Probability Ser.: Sufficient Dimension Reduction : Methods and Applications with R by Bing Li (2018, Hardcover)

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About this product

Product Identifiers

PublisherCRC Press LLC

ISBN-101498704476

ISBN-139781498704472

eBay Product ID (ePID)2309867578

Product Key Features

Number of Pages284 Pages

Publication NameSufficient Dimension Reduction : Methods and Applications with R

LanguageEnglish

SubjectProgramming Languages / General, Machine Theory, Probability & Statistics / General, Probability & Statistics / Regression Analysis, Statistics

Publication Year2018

TypeTextbook

AuthorBing Li

Subject AreaMathematics, Computers, Business & Economics

SeriesChapman and Hall/Crc Monographs on Statistics and Applied Probability Ser.

FormatHardcover

Dimensions

Item Height0.9 in

Item Weight21.9 Oz

Item Length9.5 in

Item Width6.4 in

Additional Product Features

Intended AudienceCollege Audience

LCCN2018-007862

Reviews"...Sufficient Dimension Reduction: Methods and Applications with R is a thorough overview of the key ideas and a detailed reference for advanced researchers...Professor Li gives careful discussions of the relevant details, rendering the text impressively self-contained. But as one would expect from a book based on graduate course notes, this manuscript is mainly accessible to those with advanced training in theoretical statistics...This book serves as an excellent introduction to the field of sufficient dimension reduction, and the depth of presentation and theoretical rigor are impressive. It would, of course, naturally serve as the basis for a deep graduate course, and provides a substantial foundation for anyone hoping to contribute in this thriving area." - Daniel J. McDonald, JASA 2020

Dewey Edition23

IllustratedYes

Dewey Decimal519.536028551

Table Of Content1. Dimension Reduction Subspaces 2. Sliced Inverse Regression 3. Parametric and Kernel Inverse Regression 4. Sliced Average Variance Estimate 5. Contour Regression and Directional Regression 6. Elliptical Distribution and Transformation of Predictors 7. Sufficient Dimension Reduction for Conditional Mean 8. Asymptotic Sequential Test for Order Determination 9. Other Methods for Order Determination 10. Forward Regressions for Dimension Reduction 11. Nonlinear Sufficient Dimension Reduction 12. Generalized Sliced Inverse Regression 13. Generalized Sliced Average Variance Estimator

SynopsisSufficient dimension reduction is a rapidly developing research field that has wide applications in regression diagnostics, data visualization, machine learning, genomics, image processing, pattern recognition, and medicine, because they are fields that produce large datasets with a large number of variables. Sufficient Dimension Reduction: Methods and Applications with R introduces the basic theories and the main methodologies, provides practical and easy-to-use algorithms and computer codes to implement these methodologies, and surveys the recent advances at the frontiers of this field. Features Provides comprehensive coverage of this emerging research field. Synthesizes a wide variety of dimension reduction methods under a few unifying principles such as projection in Hilbert spaces, kernel mapping, and von Mises expansion. Reflects most recent advances such as nonlinear sufficient dimension reduction, dimension folding for tensorial data, as well as sufficient dimension reduction for functional data. Includes a set of computer codes written in R that are easily implemented by the readers. Uses real data sets available online to illustrate the usage and power of the described methods. Sufficient dimension reduction has undergone momentous development in recent years, partly due to the increased demands for techniques to process high-dimensional data, a hallmark of our age of Big Data. This book will serve as the perfect entry into the field for the beginning researchers or a handy reference for the advanced ones. The author Bing Li obtained his Ph.D. from the University of Chicago. He is currently a Professor of Statistics at the Pennsylvania State University. His research interests cover sufficient dimension reduction, statistical graphical models, functional data analysis, machine learning, estimating equations and quasilikelihood, and robust statistics. He is a fellow of the Institute of Mathematical Statistics and the American Statistical Association. He is an Associate Editor for The Annals of Statistics and the Journal of the American Statistical Association ., Sufficient dimension reduction was first introduced in the early 90's as a set of graphical and diagnostic tools for regression with many predictors. Over the past two decades or so it has developed into a powerful theory and technique for handling high-dimensional data. This book will introduce the main results and important techniques in this area, and explore the current frontiers of research. These will be accompanied by numerical studies, data analysis, and computer codes.

LC Classification NumberQA278.2.L4985 2018