{"product_id":"semiparametric-regression-for-the-social-sciences-isbn-9780470319918","title":"Semiparametric Regression for the Social Sciences","description":"An introductory guide to smoothing techniques, semiparametric estimators, and their related methods, this book describes the methodology via a selection of carefully explained examples and data sets. It also demonstrates the potential of these techniques using detailed empirical examples drawn from the social and political sciences. Each chapter includes exercises and examples and there is a supplementary website containing all the datasets used, as well as computer code, allowing readers to replicate every analysis reported in the book. Includes software for implementing the methods in S-Plus and R.  List of Tables.  \u003cp\u003eList of Figures.\u003c\/p\u003e \u003cp\u003ePreface.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction: Global versus Local Statistics.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 The Consequences of Ignoring Nonlinearity.\u003c\/p\u003e \u003cp\u003e1.2 Power Transformations.\u003c\/p\u003e \u003cp\u003e1.3 Nonparametric and Semiparametric Techniques.\u003c\/p\u003e \u003cp\u003e1.4 Outline of the Text.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Smoothing and Local Regression.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Simple Smoothing.\u003c\/p\u003e \u003cp\u003e2.1.1 Local Averaging.\u003c\/p\u003e \u003cp\u003e2.1.2 Kernel Smoothing.\u003c\/p\u003e \u003cp\u003e2.2 Local Polynomial Regression.\u003c\/p\u003e \u003cp\u003e2.3 Nonparametric Modeling Choices.\u003c\/p\u003e \u003cp\u003e2.3.1 The Span.\u003c\/p\u003e \u003cp\u003e2.3.2 Polynomial Degree and Weight Function.\u003c\/p\u003e \u003cp\u003e2.3.3 A Note on Interpretation.\u003c\/p\u003e \u003cp\u003e2.4 Statistical Inference for Local Polynomial Regression.\u003c\/p\u003e \u003cp\u003e2.5 Multiple Nonparametric Regression.\u003c\/p\u003e \u003cp\u003e2.6 Conclusion.\u003c\/p\u003e \u003cp\u003e2.7 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Splines.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Simple Regression Splines.\u003c\/p\u003e \u003cp\u003e3.1.1 Basis Functions.\u003c\/p\u003e \u003cp\u003e3.2 Other Spline Models and Bases.\u003c\/p\u003e \u003cp\u003e3.2.1 Quadratic and Cubic Spline Bases.\u003c\/p\u003e \u003cp\u003e3.2.2 Natural Splines.\u003c\/p\u003e \u003cp\u003e3.2.3 B-splines.\u003c\/p\u003e \u003cp\u003e3.2.4 Knot Placement and Numbers.\u003c\/p\u003e \u003cp\u003e3.2.5 Comparing Spline Models.\u003c\/p\u003e \u003cp\u003e3.3 Splines and Overfitting.\u003c\/p\u003e \u003cp\u003e3.3.1 Smoothing Splines.\u003c\/p\u003e \u003cp\u003e3.3.2 Splines as Mixed Models.\u003c\/p\u003e \u003cp\u003e3.3.3 Final Notes on Smoothing Splines.\u003c\/p\u003e \u003cp\u003e3.3.4 Thin Plate Splines.\u003c\/p\u003e \u003cp\u003e3.4 Inference for Splines.\u003c\/p\u003e \u003cp\u003e3.5 Comparisons and Conclusions.\u003c\/p\u003e \u003cp\u003e3.6 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Automated Smoothing Techniques.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Span by Cross-Validation.\u003c\/p\u003e \u003cp\u003e4.2 Splines and Automated Smoothing.\u003c\/p\u003e \u003cp\u003e4.2.1 Estimating Smoothing Through the Likelihood.\u003c\/p\u003e \u003cp\u003e4.2.2 Smoothing Splines and Cross-Validation.\u003c\/p\u003e \u003cp\u003e4.3 Automated Smoothing in Practice.\u003c\/p\u003e \u003cp\u003e4.4 Automated Smoothing Caveats.\u003c\/p\u003e \u003cp\u003e4.5 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Additive and Semiparametric Regression Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Additive Models.\u003c\/p\u003e \u003cp\u003e5.2 Semiparametric Regression Models.\u003c\/p\u003e \u003cp\u003e5.3 Estimation.\u003c\/p\u003e \u003cp\u003e5.3.1 Backfitting.\u003c\/p\u003e \u003cp\u003e5.4 Inference.\u003c\/p\u003e \u003cp\u003e5.5 Examples.\u003c\/p\u003e \u003cp\u003e5.5.1 Congressional Elections.\u003c\/p\u003e \u003cp\u003e5.5.2 Feminist Attitudes.\u003c\/p\u003e \u003cp\u003e5.6 Discussion.\u003c\/p\u003e \u003cp\u003e5.7 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Generalized Additive Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Generalized Linear Models.\u003c\/p\u003e \u003cp\u003e6.2 Estimation of GAMS.\u003c\/p\u003e \u003cp\u003e6.3 Statistical Inference.\u003c\/p\u003e \u003cp\u003e6.4 Examples.\u003c\/p\u003e \u003cp\u003e6.4.1 Logistic Regression: The Liberal Peace.\u003c\/p\u003e \u003cp\u003e6.4.2 Ordered Logit: Domestic Violence.\u003c\/p\u003e \u003cp\u003e6.4.3 Count Models: Supreme Court Overrides.\u003c\/p\u003e \u003cp\u003e6.4.4 Survival Models: Race Riots.\u003c\/p\u003e \u003cp\u003e6.5 Discussion.\u003c\/p\u003e \u003cp\u003e6.6 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Extensions of the Semiparametric Regression Model.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Mixed Models.\u003c\/p\u003e \u003cp\u003e7.2 Bayesian Smoothing.\u003c\/p\u003e \u003cp\u003e7.3 Propensity Score Matching.\u003c\/p\u003e \u003cp\u003e7.4 Conclusion.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Bootstrapping.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Classical Inference.\u003c\/p\u003e \u003cp\u003e8.2 Bootstrapping – An Overview.\u003c\/p\u003e \u003cp\u003e8.2.1 Bootstrapping.\u003c\/p\u003e \u003cp\u003e8.2.2 An Example: Bootstrapping the Mean.\u003c\/p\u003e \u003cp\u003e8.2.3 Bootstrapping Regression Models.\u003c\/p\u003e \u003cp\u003e8.2.4 An Example: Presidential Elections.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8.3 Bootstrapping Nonparametric and Semiparametric Regression Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.3.1 Bootstrapping Nonparametric Fits.\u003c\/p\u003e \u003cp\u003e8.3.2 Bootstrapping Nonlinearity Tests.\u003c\/p\u003e \u003cp\u003e8.4 Conclusion.\u003c\/p\u003e \u003cp\u003e8.5 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Epilogue.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAppendix: Software.\u003c\/p\u003e \u003cp\u003eBibliography.\u003c\/p\u003e \u003cp\u003eAuthor Index.\u003c\/p\u003e \u003cp\u003eSubject Index.\u003c\/p\u003e \"The strength of Keele's book is that it offers clear, straightforward explanations of the models, illustrated with social science (primarily political science) applications. Applied social science researchers should be able to incorporate these methods in their own research relatively easily after reading this book.\" (\u003ci\u003eThe Political Methodologist\u003c\/i\u003e, 2009) \u003cb\u003eLuke J. Keele\u003c\/b\u003e – Department of Political Science, Ohio State University, US Since acquiring his PhD, Dr Keele has published work in a number of international journals, including papers on this specific topic. He has also taught the material for the proposed book at Ohio State University and presented it at international meetings.Dr Keele is a political scientist by trade but has considerable experience in applying statistical techniques to social science applications.  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. \u003ci\u003eSemiparametric Regression for the Social Sciences\u003c\/i\u003e sets out to address this situation by providing an accessible introduction to the subject, filled with examples drawn from the social and political sciences.  \u003cp\u003eReaders 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.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eSemiparametric Regression for the Social Sciences\u003c\/i\u003e 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.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47990006448357,"sku":"NP9780470319918","price":86.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470319918.jpg?v=1761786190","url":"https:\/\/k12savings.com\/products\/semiparametric-regression-for-the-social-sciences-isbn-9780470319918","provider":"K12savings","version":"1.0","type":"link"}