Semiparametric Regression for the Social Sciences

Nonparametric smoothing techniques allow for the estimation of nonlinear relationships between continuous variables. In conjunction with standard statistical models, these smoothing techniques provide the means to test for, and estimate, nonlinear relationships in a wide variety of analyses. Until recently these methods have been little used within the social sciences. Semiparametric Regression for the Social Sciences sets out to address this situation by providing an accessible introduction to the subject, filled with examples drawn from the social and political sciences. Readers are introduced to the principles of nonparametric smoothing and to a wide variety of smoothing methods. The author also explains how smoothing methods can be incorporated into parametric linear and generalized linear models. The use of smoothers with these standard statistical models allows the estimation of more flexible functional forms whilst retaining the interpretability of parametric models. The full potential of these techniques is highlighted via the use of detailed empirical examples drawn from the social and political sciences. Each chapter features exercises to aid in the understanding of the methods and applications. Semiparametric Regression for the Social Sciences is supported by a supplementary website containing all the datasets used and computer code for implementing the methods in S-Plus and R. The book will prove essential reading for students and researchers using statistical models in areas such as sociology, economics, psychology, demography and marketing.Keele, Luke John is the author of 'Semiparametric Regression for the Social Sciences', published 2008 under ISBN 9780470319918 and ISBN 0470319917.