Applied Regularization Methods for the Social Sciences

(Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

Researchers  in the social sciences are faced with complex data sets in which they  have relatively small samples and many variables (high dimensional  data). Unlike the various technical guides currently on the market,  Applied Regularization Methods for the Social Sciences provides and  overview of a variety of models alongside clear examples of hands-on  application. Each chapter in this book covers a specific application of  regularization techniques with a user-friendly technical description,  followed by examples that provide a thorough demonstration of the  methods in action.

Key Features
Description of regularization methods in a user friendly and easy to read manner
Inclusion  of regularization-based approaches for a variety of statistical  analyses commonly used in the social sciences, including both univariate  and multivariate models
Fully developed extended examples using multiple software packages, including R, SAS, and SPSS
Inclusion of both frequentist and Bayesian regularization approaches
Application  exercises for each chapter that instructors could use in class, and  independent researchers could use to practice what they have learned  from the book.