About this product

Product Identifiers

PublisherSpringer

ISBN-103319440470

ISBN-139783319440477

eBay Product ID (ePID)229154903

Product Key Features

Number of PagesXxv, 347 Pages

Publication NameStatistical Learning from a Regression Perspective

LanguageEnglish

Publication Year2016

SubjectPublic Health, Probability & Statistics / General, Probability & Statistics / Regression Analysis, Statistics

TypeTextbook

Subject AreaMathematics, Social Science, Medical

AuthorRichard A. Berk

SeriesSpringer Texts in Statistics Ser.

FormatHardcover

Dimensions

Item Weight31.3 Oz

Item Length9.3 in

Item Width6.1 in

Additional Product Features

Edition Number2

Intended AudienceTrade

Dewey Edition22

Reviews"The intended audience includes advanced undergraduate and graduate students biostatistics in the fields of social science and life science, as well as researchers who want to apply statistical learning procedures to scientific and policy problems. ... This is an excellent overview of statistical learning applications. It is strongly recommended to advanced researchers and statisticians particularly interested in the social and behavioral aspects of data analysis." (Puja Sitwala, Doody's Book Reviews, January, 2017)

Number of Volumes1 vol.

IllustratedYes

Dewey Decimal519.536

Table Of ContentStatistical Learning as a Regression Problem.- Splines, Smoothers, and Kernels.- Classification and Regression Trees (CART).- Bagging.- Random Forests.- Boosting.- Support Vector Machines.- Some Other Procedures Briefly.- Broader Implications and a Bit of Craft Lore.

SynopsisThis textbook considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. This fully revised new edition includes important developments over the past 8 years. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis derives from sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. As in the first edition, a unifying theme is supervised learning that can be treated as a form of regression analysis. Key concepts and procedures are illustrated with real applications, especially those with practical implications. The material is written for upper undergraduate level and graduate students in the social and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems. The author uses this book in a course on modern regression for the social, behavioral, and biological sciences. All of the analyses included are done in R with code routinely provided., Statistical Learning from a Regression Perspective considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. As a first approximation, this is can be seen as an extension of nonparametric regression. Among the statistical learning procedures examined are bagging, random forests, boosting, and support vector machines. Response variables may be quantitative or categorical. Real applications are emphasized, especially those with practical implications. One important theme is the need to explicitly take into account asymmetric costs in the fitting process. For example, in some situations false positives may be far less costly than false negatives. Another important theme is to not automatically cede modeling decisions to a fitting algorithm. In many settings, subject-matter knowledge should trump formal fitting criteria. Yet another important theme is to appreciate the limitation of one's data and not apply statistical learning procedures that require more than the data can provide. The material is written for graduate students in the social and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems. Intuitive explanations and visual representations are prominent. All of the analyses included are done in R., This book considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response., Statistical Learning from a Regression Perspective considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. As a first approximation, this is can be seen as an extension of nonparametric regression. Among the statistical learning procedures examined are bagging, random forests, boosting, and support vector machines. Response variables may be quantitative or categorical. Real applications are emphasized, especially those with practical implications. One important theme is the need to explicitly take into account asymmetric costs in the fitting process. For example, in some situations false positives may be far less costly than false negatives. Another important theme is to not automatically cede modeling decisions to a fitting algorithm. In many settings, subject-matter knowledge should trump formal fitting criteria. Yet another important theme is to appreciate the limitation of one s data and not apply statistical learning procedures that require more than the data can provide. The material is written for graduate students in the social and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems. Intuitive explanations and visual representations are prominent. All of the analyses included are done in R. "

LC Classification NumberQA276-280