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Series Editor's Introduction | 5 | |
Acknowledgments | 7 | |
1. | Introduction | 9 |
Problems in the Analysis of Event Histories | 10 | |
An Overview of Event History Methods | 11 | |
2. | A Discrete-Time Method | 14 |
A Discrete-Time Example | 15 | |
The Discrete-Time Hazard Rate | 16 | |
A Logit Regression Model | 17 | |
Estimating the Model | 18 | |
Estimates for the Biochemistry Example | 19 | |
The Likelihood-Ratio Chi-Square Test | 20 | |
Problems with the Discrete-Time Method | 21 | |
Discrete Versus Continuous Time | 22 | |
3. | Parametric Methods for Continuous-Time Data | 22 |
The Continuous-Time Hazard Rate | 23 | |
Continuous-Time Regression Models | 24 | |
Maximum Likelihood Estimation | 25 | |
An Empirical Example | 26 | |
Censoring | 28 | |
Some Other Models | 29 | |
Choosing a Model | 30 | |
Unobserved Sources of Heterogeneity | 32 | |
4. | Proportional Hazards and Partial Likelihood | 33 |
The Proportional Hazards Model | 34 | |
Partial Likelihood | 34 | |
Time-Varying Explanatory Variables | 36 | |
Adequacy of the Proportional Hazards Model | 38 | |
Choice of Origin of the Time Scale | 40 | |
Partial Likelihood for Discrete-Time Data | 41 | |
5. | Multiple Kinds of Events | 42 |
A Classification of Multiple Kinds of Events | 42 | |
Estimation for Multiple Kinds of Events | 45 | |
Models for Competing Risks | 46 | |
An Empirical Example of Competing Risks | 47 | |
Dependence Among Different Kinds of Events | 50 | |
6. | Repeated Events | 51 |
A Simple Approach | 51 | |
Problems with Repeated Events | 53 | |
Extending the Recidivism Example | 55 | |
Left Censoring | 56 | |
7. | Change of States | 57 |
Transition Rates | 58 | |
An Analysis of Job Changes | 59 | |
Simplifying the Model | 63 | |
8. | Conclusion | 65 |
Appendix A | Maximum Likelihood and Partial Likelihood | 67 |
Appendix B | Program Listings for GLIM, SAS, and BMDP Examples | 72 |
Appendix C | Computer Programs | 78 |
Notes | 81 | |
References | 83 | |
About the Author | 87 |
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Add Event History Analysis: Regression for Longitudinal Event Data, Drawing on recent event history analytical methods from biostatistics, engineering, and sociology, this clear and comprehensive monograph explains how longitudinal data can be used to study the causes of deaths, crimes, wars, and many other human events, Event History Analysis: Regression for Longitudinal Event Data to the inventory that you are selling on WonderClubX
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Add Event History Analysis: Regression for Longitudinal Event Data, Drawing on recent event history analytical methods from biostatistics, engineering, and sociology, this clear and comprehensive monograph explains how longitudinal data can be used to study the causes of deaths, crimes, wars, and many other human events, Event History Analysis: Regression for Longitudinal Event Data to your collection on WonderClub |