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Semiparametric regression for the social sciences [[electronic resource] /] / Luke Keele
Semiparametric regression for the social sciences [[electronic resource] /] / Luke Keele
Autore Keele Luke <1974->
Pubbl/distr/stampa Chichester, England ; ; Hoboken, NJ, : Wiley, c2008
Descrizione fisica 1 online resource (231 p.)
Disciplina 300.1519536
519.5/36
519.536
Soggetto topico Regression analysis
Nonparametric statistics
Soggetto genere / forma Electronic books.
ISBN 1-281-31237-1
9786611312374
0-470-99813-X
0-470-99812-1
Classificazione 31.73
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Semiparametric Regression for the Social Sciences; Contents; List of Tables; List of Figures; Preface; 1 Introduction: Global versus 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 andWeight Function; 2.3.3 A Note on Interpretation
2.4 Statistical Inference for Local Polynomial Regression2.5 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 Over.tting; 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 Techniques4.1 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 Back.tting; 5.4 Inference; 5.5 Examples; 5.5.1 Congressional Elections; 5.5.2 Feminist Attitudes; 5.6 Discussion; 5.7 Exercises; 6 Generalized Additive Models
6.1 Generalized Linear Models6.2 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 Models8.2.4 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; 9 Epilogue; Appendix: Software; Bibliography; Author's Index; Subject Index
Record Nr. UNINA-9910144715103321
Keele Luke <1974->  
Chichester, England ; ; Hoboken, NJ, : Wiley, c2008
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Semiparametric regression for the social sciences [[electronic resource] /] / Luke Keele
Semiparametric regression for the social sciences [[electronic resource] /] / Luke Keele
Autore Keele Luke <1974->
Pubbl/distr/stampa Chichester, England ; ; Hoboken, NJ, : Wiley, c2008
Descrizione fisica 1 online resource (231 p.)
Disciplina 300.1519536
519.5/36
519.536
Soggetto topico Regression analysis
Nonparametric statistics
ISBN 1-281-31237-1
9786611312374
0-470-99813-X
0-470-99812-1
Classificazione 31.73
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Semiparametric Regression for the Social Sciences; Contents; List of Tables; List of Figures; Preface; 1 Introduction: Global versus 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 andWeight Function; 2.3.3 A Note on Interpretation
2.4 Statistical Inference for Local Polynomial Regression2.5 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 Over.tting; 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 Techniques4.1 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 Back.tting; 5.4 Inference; 5.5 Examples; 5.5.1 Congressional Elections; 5.5.2 Feminist Attitudes; 5.6 Discussion; 5.7 Exercises; 6 Generalized Additive Models
6.1 Generalized Linear Models6.2 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 Models8.2.4 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; 9 Epilogue; Appendix: Software; Bibliography; Author's Index; Subject Index
Record Nr. UNINA-9910829940503321
Keele Luke <1974->  
Chichester, England ; ; Hoboken, NJ, : Wiley, c2008
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui