Semiparametric regression for the social sciences:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Chichester, England
Wiley
2008
|
| Schlagworte: | |
| Online-Zugang: | Publisher description Table of contents only Contributor biographical information Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references (p. [203]-207) and indexes |
| Beschreibung: | xvi, 213 p. ill. 24 cm |
| ISBN: | 9780470319918 0470319917 |
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Datensatz im Suchindex
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|---|---|
| adam_text | Titel: Semiparametric regression for the social sciences
Autor: Keele, Luke
Jahr: 2008
Contents
List of Tables ix
List of Figures xi
Preface xv
1 Introduction: Global versus Local Statistics 1
1.1 The Consequences of Ignoring Nonlinearity................. 4
1.2 Power Transformations............................... 5
1.3 Nonparametric and Semiparametric Techniques.............. 8
1.4 Outline of the Text.................................. 12
2 Smoothing and Local Regression 13
2.1 Simple Smoothing.................................. 14
2.1.1 Local Averaging.............................. 15
2.1.2 Kernel Smoothing............................. 21
2.2 Local Polynomial Regression.......................... 26
2.3 Nonparametric Modeling Choices....................... 31
2.3.1 The Span................................... 31
2.3.2 Polynomial Degree and Weight Function............. 34
2.3.3 A Note on Interpretation........................ 36
2.4 Statistical Inference for Local Polynomial Regression......... 39
2.5 Multiple Nonparametric Regression...................... 45
2.6 Conclusion....................................... 47
2.7 Exercises........................................ 47
3 Splines 49
3.1 Simple Regression Splines............................ 50
3.1.1 Basis Functions.............................. 52
3.2 Other Spline Models and Bases........................ 54
3.2.1 Quadratic and Cubic Spline Bases................. 55
3.2.2 Natural Splines............................... 57
3.2.3 B-splines................................... 58
3.2.4 Knot Placement and Numbers.................... 59
3.2.5 Comparing Spline Models....................... 61
3.3 Splines and Overfitting............................... 62
3.3.1 Smoothing Splines............................ 64
3.3.2 Splines as Mixed Models........................ 69
3.3.3 Final Notes on Smoothing Splines................. 73
3.3.4 Thin Plate Splines............................. 74
3.4 Inference for Splines................................ 75
3.5 Comparisons and Conclusions......................... 80
3.6 Exercises........................................ 82
4 Automated Smoothing Techniques 85
4.1 Span by Cross-Validation............................. 86
4.2 Splines and Automated Smoothing...................... 89
4.2.1 Estimating Smoothing Through the Likelihood........ 90
4.2.2 Smoothing Splines and Cross-Validation............. 90
4.3 Automated Smoothing in Practice....................... 93
4.4 Automated Smoothing Caveats......................... 105
4.5 Exercises........................................ 107
5 Additive and Semiparametric Regression Models 109
5.1 Additive Models................................... 110
5.2 Semiparametric Regression Models...................... 112
5.3 Estimation....................................... 113
5.3.1 Backfitting.................................. 113
5.4 Inference........................................ 117
5.5 Exampies........................................ 120
5.5.1 Congressional Elections......................... 120
5.5.2 Feminist Attitudes............................. 129
5.6 Discussion....................................... 133
5.7 Exercises........................................ 134
6 Generalized Additive Models 137
6.1 Generalized Linear Models............................ 137
6.2 Estimation of GAMS................................ 140
6.3 Statistical Inference................................. 141
6.4 Exampies........................................ 143
6.4.1 Logistic Regression: The Liberal Peace.............. 143
6.4.2 Ordered Logit: Domestic Violence................. 146
6.4.3 Count Models: Supreme Court Overrides............ 150
6.4.4 Survival Models: Race Riots..................... 154
6.5 Discussion....................................... 157
6.6 Exercises........................................ 157
7 Extensions of the Semiparametric Regression Model 161
7.1 Mixed Models..................................... 162
7.2 Bayesian Smoothing................................ 166
7.3 Propensity Score Matching............................ 170
7.4 Conclusion....................................... 175
8 Bootstrapping 177
8.1 Classical Inference................................. 178
8.2 Bootstrapping - An Overview.......................... 178
8.2.1 Bootstrapping................................ 179
8.2.2 An Example: Bootstrapping the Mean............... 183
8.2.3 Bootstrapping Regression Models.................. 185
8.2.4 An Example: Presidential Elections................ 187
8.3 Bootstrapping Nonparametric and Semiparametric
Regression Models................................. 189
8.3.1 Bootstrapping Nonparametric Fits.................. 189
8.3.2 Bootstrapping Nonlinearity Tests.................. 190
8.4 Conclusion....................................... 192
8.5 Exercises........................................ 193
9 Epilogue 195
Appendix: Software 197
Bibliography 203
Author Index 209
Subject Index 211
|
| adam_txt |
Titel: Semiparametric regression for the social sciences
Autor: Keele, Luke
Jahr: 2008
Contents
List of Tables ix
List of Figures xi
Preface xv
1 Introduction: Global versus Local Statistics 1
1.1 The Consequences of Ignoring Nonlinearity. 4
1.2 Power Transformations. 5
1.3 Nonparametric and Semiparametric Techniques. 8
1.4 Outline of the Text. 12
2 Smoothing and Local Regression 13
2.1 Simple Smoothing. 14
2.1.1 Local Averaging. 15
2.1.2 Kernel Smoothing. 21
2.2 Local Polynomial Regression. 26
2.3 Nonparametric Modeling Choices. 31
2.3.1 The Span. 31
2.3.2 Polynomial Degree and Weight Function. 34
2.3.3 A Note on Interpretation. 36
2.4 Statistical Inference for Local Polynomial Regression. 39
2.5 Multiple Nonparametric Regression. 45
2.6 Conclusion. 47
2.7 Exercises. 47
3 Splines 49
3.1 Simple Regression Splines. 50
3.1.1 Basis Functions. 52
3.2 Other Spline Models and Bases. 54
3.2.1 Quadratic and Cubic Spline Bases. 55
3.2.2 Natural Splines. 57
3.2.3 B-splines. 58
3.2.4 Knot Placement and Numbers. 59
3.2.5 Comparing Spline Models. 61
3.3 Splines and Overfitting. 62
3.3.1 Smoothing Splines. 64
3.3.2 Splines as Mixed Models. 69
3.3.3 Final Notes on Smoothing Splines. 73
3.3.4 Thin Plate Splines. 74
3.4 Inference for Splines. 75
3.5 Comparisons and Conclusions. 80
3.6 Exercises. 82
4 Automated Smoothing Techniques 85
4.1 Span by Cross-Validation. 86
4.2 Splines and Automated Smoothing. 89
4.2.1 Estimating Smoothing Through the Likelihood. 90
4.2.2 Smoothing Splines and Cross-Validation. 90
4.3 Automated Smoothing in Practice. 93
4.4 Automated Smoothing Caveats. 105
4.5 Exercises. 107
5 Additive and Semiparametric Regression Models 109
5.1 Additive Models. 110
5.2 Semiparametric Regression Models. 112
5.3 Estimation. 113
5.3.1 Backfitting. 113
5.4 Inference. 117
5.5 Exampies. 120
5.5.1 Congressional Elections. 120
5.5.2 Feminist Attitudes. 129
5.6 Discussion. 133
5.7 Exercises. 134
6 Generalized Additive Models 137
6.1 Generalized Linear Models. 137
6.2 Estimation of GAMS. 140
6.3 Statistical Inference. 141
6.4 Exampies. 143
6.4.1 Logistic Regression: The Liberal Peace. 143
6.4.2 Ordered Logit: Domestic Violence. 146
6.4.3 Count Models: Supreme Court Overrides. 150
6.4.4 Survival Models: Race Riots. 154
6.5 Discussion. 157
6.6 Exercises. 157
7 Extensions of the Semiparametric Regression Model 161
7.1 Mixed Models. 162
7.2 Bayesian Smoothing. 166
7.3 Propensity Score Matching. 170
7.4 Conclusion. 175
8 Bootstrapping 177
8.1 Classical Inference. 178
8.2 Bootstrapping - An Overview. 178
8.2.1 Bootstrapping. 179
8.2.2 An Example: Bootstrapping the Mean. 183
8.2.3 Bootstrapping Regression Models. 185
8.2.4 An Example: Presidential Elections. 187
8.3 Bootstrapping Nonparametric and Semiparametric
Regression Models. 189
8.3.1 Bootstrapping Nonparametric Fits. 189
8.3.2 Bootstrapping Nonlinearity Tests. 190
8.4 Conclusion. 192
8.5 Exercises. 193
9 Epilogue 195
Appendix: Software 197
Bibliography 203
Author Index 209
Subject Index 211 |
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| spellingShingle | Keele, Luke 1974- Semiparametric regression for the social sciences Regression analysis Nonparametric statistics Semiparametrisches Verfahren (DE-588)7576374-6 gnd Regressionsanalyse (DE-588)4129903-6 gnd |
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| title | Semiparametric regression for the social sciences |
| title_auth | Semiparametric regression for the social sciences |
| title_exact_search | Semiparametric regression for the social sciences |
| title_exact_search_txtP | Semiparametric regression for the social sciences |
| title_full | Semiparametric regression for the social sciences Luke Keele |
| title_fullStr | Semiparametric regression for the social sciences Luke Keele |
| title_full_unstemmed | Semiparametric regression for the social sciences Luke Keele |
| title_short | Semiparametric regression for the social sciences |
| title_sort | semiparametric regression for the social sciences |
| topic | Regression analysis Nonparametric statistics Semiparametrisches Verfahren (DE-588)7576374-6 gnd Regressionsanalyse (DE-588)4129903-6 gnd |
| topic_facet | Regression analysis Nonparametric statistics Semiparametrisches Verfahren Regressionsanalyse |
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