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.

Accessible discussion of statistical learning procedures for practitioners with real-world applications in the social and policy sciences Methods also of interest in the natural sciences and engineering Fully revised new edition with intuitive explanations and visual representation of underlying statistical concepts Includes supplementary material: sn.pub/extras

Autorentext

Richard Berk is Distinguished Professor of Statistics Emeritus from the Department of Statistics at UCLA and currently a Professor at the University of Pennsylvania in the Department of Statistics and in the Department of Criminology. He is an elected fellow of the American Statistical Association and the American Association for the Advancement of Science and has served in a professional capacity with a number of organizations such as the Committee on Applied and Theoretical Statistics for the National Research Council and the Board of Directors of the Social Science Research Council. His research has ranged across a variety of applications in the social and natural sciences.

Inhalt
Statistical 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.