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Semiparametric regression for the social sciences

Author: Luke Keele
Publisher: Chichester, England ; Hoboken, NJ : Wiley, ©2008.
Edition/Format:   Print book : EnglishView all editions and formats
Database:WorldCat
Summary:
"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  Read more...
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Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Luke Keele
ISBN: 9780470319918 0470319917
OCLC Number: 181079183
Description: xvi, 213 pages : illustrations ; 24 cm
Contents: Preface. 1. Introduction: Global vs Local Statistics. 1.1 The Consequences of Ignoring Nonlinearity. 1.2 Power Transformations. 1.3 Nonparametric and Semiparametric Techniques. 1.4 Outline Of The Text. 2. Smoothing and Local Regression. 2.1 Simple Smoothing. 2.1.1 Local Averaging. 2.1.2 Kernel Smoothing. 2.2 Local Polynomial Regression. 2.3 Nonparametric Modeling Choices. 2.3.1 The Span. 2.3.2 Polynomial Degree and Weight Function. 2.3.3 A Note On Interpretation. 2.4 Statistical Inference for Local Polynomial Regression. 2.5 Multiple Nonparametric Regression. 2.6 Conclusion. 2.7 Exercises. 3. Splines. 3.1 Simple Regression Splines. 3.1.1 Basis Functions. 3.2 Other Spline Models and Bases. 3.2.1 Quadratic and Cubic Spline Bases. 3.2.2 Natural Splines. 3.2.3 B-splines. 3.2.4 Knot Placement and Numbers. 3.2.5 Comparing Spline Models. 3.3 Splines and Overfitting. 3.3.1 Smoothing Splines. 3.3.2 Splines as Mixed Models. 3.3.3 Final Notes on Smoothing Splines. 3.3.4 Thin plate splines. 3.4 Inference for Splines. 3.5 Comparisons And Conclusions. 3.6 Exercises. 4. Automated Smoothing Techniques. 4.1 Span By Cross-Validation. 4.2 Splines and Automated Smoothing. 4.2.1 Estimating Smoothing Through the Likelihood. 4.2.2 Smoothing Splines and Cross-Validation. 4.3 Automated Smoothing in Practice. 4.4 Automated Smoothing Caveats. 4.5 Exercises. 5. Additive and Semiparametric Regression Models. 5.1 Additive Models. 5.2 Semiparametric Regression Models. 5.3 Estimation. 5.3.1 Backfitting. 5.4 Inference. 5.5 Examples. 5.5.1 Congressional Voting. 5.5.2 Example: Feminist Attitudes. 5.6 Discussion. 5.7 Exercises. 6. Generalized Additive Models. 6.1 Generalized Linear Models. 6.2 Estimation of GAMS. 6.3 Statistical Inference. 6.4 Examples. 6.4.1 Logistic Regression: The Liberal Peace. 6.4.2 Ordered Logit: Domestic Violence. 6.4.3 Count Models: Supreme Court Overrides. 6.4.4 Survival Models: Race Riots. 6.5 Discussion. 6.6 Exercises. 7. Extensions of the Semiparametric Regression Model. 7.1 Mixed Models. 7.2 Bayesian Smoothing. 7.3 Propensity Score Matching. 7.4 Conclusion. 8. Bootstrapping. 8.1 Classical Inference. 8.2 Bootstrapping --
An Overview. 8.2.1 Bootstrapping. 8.2.2 An Example: Bootstrapping the Mean. 8.2.3 Bootstrapping Regression Models. 8.2.4 An Example: Presidential Elections. 8.3 Bootstrapping Nonparametric and Semiparametric Regression Models. 8.3.1 Bootstrapping Nonparametric Fits. 8.3.2 Bootstrapping Nonlinearity Tests. 8.4 Conclusion. 8.5 Exercises. Epilogue. Appendix: Software.
Responsibility: Luke Keele.
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Abstract:

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.  Read more...

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"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 Read more...

 
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   schema:reviewBody ""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." "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."--Jacket." ;
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