- Gebundenes Buch
- Merkliste
- Auf die Merkliste
- Bewerten Bewerten
- Teilen
- Produkt teilen
- Produkterinnerung
- Produkterinnerung
Die erste Auflage dieses Buches erschien bereits vor über zwei Jahrzehnten - für die Autoren Grund genug, ihren nach wie vor gefragten Band zu überarbeiten und zu aktualisieren. Die Themen wurden neu gewichtet, das Spektrum wurde erweitert. Hauptentwicklungen, Schwerpunkte und Trends werden anschaulich diskutiert. Abgerundet wird der Text durch eine ausführliche Bibliographie und zahlreiche Übungsaufgaben.
Andere Kunden interessierten sich auch für
- Thomas R. FlemingCounting Processes and Survival Analysis154,99 €
- Thomas P. RyanModern Experimental Design204,99 €
- Jerald F. LawlessStatistical Models and Methods for Lifetime Data209,99 €
- Bovas AbrahamStatistical Methods for Forecasting178,99 €
- Niels Keiding / Per Kragh Andersen (eds.)Survival and Event History Analysis311,99 €
- Yasunori FujikoshiMultivariate Statistics168,99 €
- Ton de WaalHandbook of Statistical Data Editing and Imputation207,99 €
-
-
-
Die erste Auflage dieses Buches erschien bereits vor über zwei Jahrzehnten - für die Autoren Grund genug, ihren nach wie vor gefragten Band zu überarbeiten und zu aktualisieren. Die Themen wurden neu gewichtet, das Spektrum wurde erweitert. Hauptentwicklungen, Schwerpunkte und Trends werden anschaulich diskutiert. Abgerundet wird der Text durch eine ausführliche Bibliographie und zahlreiche Übungsaufgaben.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Produktdetails
- Produktdetails
- Wiley Series in Probability and Statistics
- Verlag: Wiley & Sons
- 2. Aufl.
- Seitenzahl: 462
- Erscheinungstermin: 9. September 2002
- Englisch
- Abmessung: 240mm x 161mm x 29mm
- Gewicht: 770g
- ISBN-13: 9780471363576
- ISBN-10: 047136357X
- Artikelnr.: 10539728
- Wiley Series in Probability and Statistics
- Verlag: Wiley & Sons
- 2. Aufl.
- Seitenzahl: 462
- Erscheinungstermin: 9. September 2002
- Englisch
- Abmessung: 240mm x 161mm x 29mm
- Gewicht: 770g
- ISBN-13: 9780471363576
- ISBN-10: 047136357X
- Artikelnr.: 10539728
JOHN D. KALBFLEISCH, PhD, is Professor of Biostatistics at the University of Michigan in Ann Arbor and the University of Waterloo in Ontario, Canada. ROSS L. PRENTICE, PhD, is Professor of Biostatistics at the Fred Hutchinson Cancer Research Center and the University of Washington in Seattle.
Preface. 1. Introduction. 1.1 Failure Time Data. 1.2 Failure Time
Distributions. 1.3 Time Origins, Censoring, and Truncation. 1.4 Estimation
of the Survivor Function. 1.5 Comparison of Survival Curves. 1.6
Generalizations to Accommodate Delayed Entry. 1.7 Counting Process
Notation. Bibliographic Notes. Exercises and Complements. 2. Failure Time
Models. 2.1 Introduction. 2.2 Some Continuous Parametric Failure Time
Models. 2.3 Regression Models. 2.4 Discrete Failure Time Models.
Bibliographic Notes. Exercises and Complements. 3. Inference in Parametric
Models and Related Topics. 3.1 Introduction. 3.2 Censoring Mechanisms. 3.3
Censored Samples from an Exponential Distribution. 3.4 Large-Sample
Likelihood Theory. 3.5 Exponential Regression. 3.6 Estimation in Log-Linear
Regression Models. 3.7 Illustrations in More Complex Data Sets. 3.8
Discrimination Among Parametric Models. 3.9 Inference with Interval
Censoring. 3.10 Discussion. Bibliographic Notes. Exercises and Complements.
4. Relative Risk (Cox) Regression Models. 4.1 Introduction. 4.2 Estimation
of . 4.3 Estimation of the Baseline Hazard or Survivor Function. 4.4
Inclusion of Strata. 4.5 Illustrations. 4.6 Counting Process Formulas. 4.7
Related Topics on the Cox Model. 4.8 Sampling from Discrete Models.
Bibliographic Notes. Exercises and Complements. 5. Counting Processes and
Asymptotic Theory. 5.1 Introduction. 5.2 Counting Processes and Intensity
Functions. 5.3 Martingales. 5.4 Vector-Valued Martingales. 5.5 Martingale
Central Limit Theorem. 5.6 Asymptotics Associated with Chapter 1. 5.7
Asymptotic Results for the Cox Model. 5.8 Asymptotic Results for Parametric
Models. 5.9 Efficiency of the Cox Model Estimator. 5.10 Partial Likelihood
Filtration. Bibliographic Notes. Exercises and Complements. 6. Likelihood
Construction and Further Results. 6.1 Introduction. 6.2 Likelihood
Construction in Parametric Models. 6.3 Time-Dependent Covariates and
Further Remarks on Likelihood Construction. 6.4 Time Dependence in the
Relative Risk Model. 6.5 Nonnested Conditioning Events. 6.6 Residuals and
Model Checking for the Cox Model. Bibliographic Notes. Exercises and
Complements. 7. Rank Regression and the Accelerated Failure Time Model. 7.1
Introduction. 7.2 Linear Rank Tests. 7.3 Development and Properties of
Linear Rank Tests. 7.4 Estimation in the Accelerated Failure Time Model.
7.5 Some Related Regression Models. Bibliographic Notes. Exercises and
Complements. 8. Competing Risks and Multistate Models. 8.1 Introduction.
8.2 Competing Risks. 8.3 Life-History Processes. Bibliographic Notes.
Exercises and Complements. 9. Modeling and Analysis of Recurrent Event
Data. 9.1 Introduction. 9.2 Intensity Processes for Recurrent Events. 9.3
Overall Intensity Process Modeling and Estimation. 9.4 Mean Process
Modeling and Estimation. 9.5 Conditioning on Aspects of the Counting
Process History. Bibliographic Notes. Exercises and Complements. 10.
Analysis of Correlated Failure Time Data. 10.1 Introduction. 10.2
Regression Models for Correlated Failure Time Data. 10.3 Representation and
Estimation of the Bivariate Survivor Function. 10.4 Pairwise Dependency
Estimation. 10.5 Illustration: Australian Twin Data. 10.6 Approaches to
Nonparametric Estimation of the Bivariate Survivor Function. 10.7 Survivor
Function Estimation in Higher Dimensions. Bibliographic Notes. Exercises
and Complements. 11. Additional Failure Time Data Topics. 11.1
Introduction. 11.2 Stratified Bivariate Failure Time Analysis. 11.3 Fixed
Study Period Survival Studies. 11.4 Cohort Sampling and Case-Control
Studies. 11.5 Missing Covariate Data. 11.6 Mismeasured Covariate Data. 11.7
Sequential Testing with Failure Time Endpoints. 11.8 Bayesian Analysis of
the Proportional Hazards Model. 11.9 Some Analyses of a Particular Data
Set. Bibliographic Notes. Exercises and Complements. Glossary of Notation.
Appendix A: Some Sets of Data. Appendix B: Supporting Technical Material.
Bibliography. Author Index. Subject Index.
Distributions. 1.3 Time Origins, Censoring, and Truncation. 1.4 Estimation
of the Survivor Function. 1.5 Comparison of Survival Curves. 1.6
Generalizations to Accommodate Delayed Entry. 1.7 Counting Process
Notation. Bibliographic Notes. Exercises and Complements. 2. Failure Time
Models. 2.1 Introduction. 2.2 Some Continuous Parametric Failure Time
Models. 2.3 Regression Models. 2.4 Discrete Failure Time Models.
Bibliographic Notes. Exercises and Complements. 3. Inference in Parametric
Models and Related Topics. 3.1 Introduction. 3.2 Censoring Mechanisms. 3.3
Censored Samples from an Exponential Distribution. 3.4 Large-Sample
Likelihood Theory. 3.5 Exponential Regression. 3.6 Estimation in Log-Linear
Regression Models. 3.7 Illustrations in More Complex Data Sets. 3.8
Discrimination Among Parametric Models. 3.9 Inference with Interval
Censoring. 3.10 Discussion. Bibliographic Notes. Exercises and Complements.
4. Relative Risk (Cox) Regression Models. 4.1 Introduction. 4.2 Estimation
of . 4.3 Estimation of the Baseline Hazard or Survivor Function. 4.4
Inclusion of Strata. 4.5 Illustrations. 4.6 Counting Process Formulas. 4.7
Related Topics on the Cox Model. 4.8 Sampling from Discrete Models.
Bibliographic Notes. Exercises and Complements. 5. Counting Processes and
Asymptotic Theory. 5.1 Introduction. 5.2 Counting Processes and Intensity
Functions. 5.3 Martingales. 5.4 Vector-Valued Martingales. 5.5 Martingale
Central Limit Theorem. 5.6 Asymptotics Associated with Chapter 1. 5.7
Asymptotic Results for the Cox Model. 5.8 Asymptotic Results for Parametric
Models. 5.9 Efficiency of the Cox Model Estimator. 5.10 Partial Likelihood
Filtration. Bibliographic Notes. Exercises and Complements. 6. Likelihood
Construction and Further Results. 6.1 Introduction. 6.2 Likelihood
Construction in Parametric Models. 6.3 Time-Dependent Covariates and
Further Remarks on Likelihood Construction. 6.4 Time Dependence in the
Relative Risk Model. 6.5 Nonnested Conditioning Events. 6.6 Residuals and
Model Checking for the Cox Model. Bibliographic Notes. Exercises and
Complements. 7. Rank Regression and the Accelerated Failure Time Model. 7.1
Introduction. 7.2 Linear Rank Tests. 7.3 Development and Properties of
Linear Rank Tests. 7.4 Estimation in the Accelerated Failure Time Model.
7.5 Some Related Regression Models. Bibliographic Notes. Exercises and
Complements. 8. Competing Risks and Multistate Models. 8.1 Introduction.
8.2 Competing Risks. 8.3 Life-History Processes. Bibliographic Notes.
Exercises and Complements. 9. Modeling and Analysis of Recurrent Event
Data. 9.1 Introduction. 9.2 Intensity Processes for Recurrent Events. 9.3
Overall Intensity Process Modeling and Estimation. 9.4 Mean Process
Modeling and Estimation. 9.5 Conditioning on Aspects of the Counting
Process History. Bibliographic Notes. Exercises and Complements. 10.
Analysis of Correlated Failure Time Data. 10.1 Introduction. 10.2
Regression Models for Correlated Failure Time Data. 10.3 Representation and
Estimation of the Bivariate Survivor Function. 10.4 Pairwise Dependency
Estimation. 10.5 Illustration: Australian Twin Data. 10.6 Approaches to
Nonparametric Estimation of the Bivariate Survivor Function. 10.7 Survivor
Function Estimation in Higher Dimensions. Bibliographic Notes. Exercises
and Complements. 11. Additional Failure Time Data Topics. 11.1
Introduction. 11.2 Stratified Bivariate Failure Time Analysis. 11.3 Fixed
Study Period Survival Studies. 11.4 Cohort Sampling and Case-Control
Studies. 11.5 Missing Covariate Data. 11.6 Mismeasured Covariate Data. 11.7
Sequential Testing with Failure Time Endpoints. 11.8 Bayesian Analysis of
the Proportional Hazards Model. 11.9 Some Analyses of a Particular Data
Set. Bibliographic Notes. Exercises and Complements. Glossary of Notation.
Appendix A: Some Sets of Data. Appendix B: Supporting Technical Material.
Bibliography. Author Index. Subject Index.
Preface. 1. Introduction. 1.1 Failure Time Data. 1.2 Failure Time
Distributions. 1.3 Time Origins, Censoring, and Truncation. 1.4 Estimation
of the Survivor Function. 1.5 Comparison of Survival Curves. 1.6
Generalizations to Accommodate Delayed Entry. 1.7 Counting Process
Notation. Bibliographic Notes. Exercises and Complements. 2. Failure Time
Models. 2.1 Introduction. 2.2 Some Continuous Parametric Failure Time
Models. 2.3 Regression Models. 2.4 Discrete Failure Time Models.
Bibliographic Notes. Exercises and Complements. 3. Inference in Parametric
Models and Related Topics. 3.1 Introduction. 3.2 Censoring Mechanisms. 3.3
Censored Samples from an Exponential Distribution. 3.4 Large-Sample
Likelihood Theory. 3.5 Exponential Regression. 3.6 Estimation in Log-Linear
Regression Models. 3.7 Illustrations in More Complex Data Sets. 3.8
Discrimination Among Parametric Models. 3.9 Inference with Interval
Censoring. 3.10 Discussion. Bibliographic Notes. Exercises and Complements.
4. Relative Risk (Cox) Regression Models. 4.1 Introduction. 4.2 Estimation
of . 4.3 Estimation of the Baseline Hazard or Survivor Function. 4.4
Inclusion of Strata. 4.5 Illustrations. 4.6 Counting Process Formulas. 4.7
Related Topics on the Cox Model. 4.8 Sampling from Discrete Models.
Bibliographic Notes. Exercises and Complements. 5. Counting Processes and
Asymptotic Theory. 5.1 Introduction. 5.2 Counting Processes and Intensity
Functions. 5.3 Martingales. 5.4 Vector-Valued Martingales. 5.5 Martingale
Central Limit Theorem. 5.6 Asymptotics Associated with Chapter 1. 5.7
Asymptotic Results for the Cox Model. 5.8 Asymptotic Results for Parametric
Models. 5.9 Efficiency of the Cox Model Estimator. 5.10 Partial Likelihood
Filtration. Bibliographic Notes. Exercises and Complements. 6. Likelihood
Construction and Further Results. 6.1 Introduction. 6.2 Likelihood
Construction in Parametric Models. 6.3 Time-Dependent Covariates and
Further Remarks on Likelihood Construction. 6.4 Time Dependence in the
Relative Risk Model. 6.5 Nonnested Conditioning Events. 6.6 Residuals and
Model Checking for the Cox Model. Bibliographic Notes. Exercises and
Complements. 7. Rank Regression and the Accelerated Failure Time Model. 7.1
Introduction. 7.2 Linear Rank Tests. 7.3 Development and Properties of
Linear Rank Tests. 7.4 Estimation in the Accelerated Failure Time Model.
7.5 Some Related Regression Models. Bibliographic Notes. Exercises and
Complements. 8. Competing Risks and Multistate Models. 8.1 Introduction.
8.2 Competing Risks. 8.3 Life-History Processes. Bibliographic Notes.
Exercises and Complements. 9. Modeling and Analysis of Recurrent Event
Data. 9.1 Introduction. 9.2 Intensity Processes for Recurrent Events. 9.3
Overall Intensity Process Modeling and Estimation. 9.4 Mean Process
Modeling and Estimation. 9.5 Conditioning on Aspects of the Counting
Process History. Bibliographic Notes. Exercises and Complements. 10.
Analysis of Correlated Failure Time Data. 10.1 Introduction. 10.2
Regression Models for Correlated Failure Time Data. 10.3 Representation and
Estimation of the Bivariate Survivor Function. 10.4 Pairwise Dependency
Estimation. 10.5 Illustration: Australian Twin Data. 10.6 Approaches to
Nonparametric Estimation of the Bivariate Survivor Function. 10.7 Survivor
Function Estimation in Higher Dimensions. Bibliographic Notes. Exercises
and Complements. 11. Additional Failure Time Data Topics. 11.1
Introduction. 11.2 Stratified Bivariate Failure Time Analysis. 11.3 Fixed
Study Period Survival Studies. 11.4 Cohort Sampling and Case-Control
Studies. 11.5 Missing Covariate Data. 11.6 Mismeasured Covariate Data. 11.7
Sequential Testing with Failure Time Endpoints. 11.8 Bayesian Analysis of
the Proportional Hazards Model. 11.9 Some Analyses of a Particular Data
Set. Bibliographic Notes. Exercises and Complements. Glossary of Notation.
Appendix A: Some Sets of Data. Appendix B: Supporting Technical Material.
Bibliography. Author Index. Subject Index.
Distributions. 1.3 Time Origins, Censoring, and Truncation. 1.4 Estimation
of the Survivor Function. 1.5 Comparison of Survival Curves. 1.6
Generalizations to Accommodate Delayed Entry. 1.7 Counting Process
Notation. Bibliographic Notes. Exercises and Complements. 2. Failure Time
Models. 2.1 Introduction. 2.2 Some Continuous Parametric Failure Time
Models. 2.3 Regression Models. 2.4 Discrete Failure Time Models.
Bibliographic Notes. Exercises and Complements. 3. Inference in Parametric
Models and Related Topics. 3.1 Introduction. 3.2 Censoring Mechanisms. 3.3
Censored Samples from an Exponential Distribution. 3.4 Large-Sample
Likelihood Theory. 3.5 Exponential Regression. 3.6 Estimation in Log-Linear
Regression Models. 3.7 Illustrations in More Complex Data Sets. 3.8
Discrimination Among Parametric Models. 3.9 Inference with Interval
Censoring. 3.10 Discussion. Bibliographic Notes. Exercises and Complements.
4. Relative Risk (Cox) Regression Models. 4.1 Introduction. 4.2 Estimation
of . 4.3 Estimation of the Baseline Hazard or Survivor Function. 4.4
Inclusion of Strata. 4.5 Illustrations. 4.6 Counting Process Formulas. 4.7
Related Topics on the Cox Model. 4.8 Sampling from Discrete Models.
Bibliographic Notes. Exercises and Complements. 5. Counting Processes and
Asymptotic Theory. 5.1 Introduction. 5.2 Counting Processes and Intensity
Functions. 5.3 Martingales. 5.4 Vector-Valued Martingales. 5.5 Martingale
Central Limit Theorem. 5.6 Asymptotics Associated with Chapter 1. 5.7
Asymptotic Results for the Cox Model. 5.8 Asymptotic Results for Parametric
Models. 5.9 Efficiency of the Cox Model Estimator. 5.10 Partial Likelihood
Filtration. Bibliographic Notes. Exercises and Complements. 6. Likelihood
Construction and Further Results. 6.1 Introduction. 6.2 Likelihood
Construction in Parametric Models. 6.3 Time-Dependent Covariates and
Further Remarks on Likelihood Construction. 6.4 Time Dependence in the
Relative Risk Model. 6.5 Nonnested Conditioning Events. 6.6 Residuals and
Model Checking for the Cox Model. Bibliographic Notes. Exercises and
Complements. 7. Rank Regression and the Accelerated Failure Time Model. 7.1
Introduction. 7.2 Linear Rank Tests. 7.3 Development and Properties of
Linear Rank Tests. 7.4 Estimation in the Accelerated Failure Time Model.
7.5 Some Related Regression Models. Bibliographic Notes. Exercises and
Complements. 8. Competing Risks and Multistate Models. 8.1 Introduction.
8.2 Competing Risks. 8.3 Life-History Processes. Bibliographic Notes.
Exercises and Complements. 9. Modeling and Analysis of Recurrent Event
Data. 9.1 Introduction. 9.2 Intensity Processes for Recurrent Events. 9.3
Overall Intensity Process Modeling and Estimation. 9.4 Mean Process
Modeling and Estimation. 9.5 Conditioning on Aspects of the Counting
Process History. Bibliographic Notes. Exercises and Complements. 10.
Analysis of Correlated Failure Time Data. 10.1 Introduction. 10.2
Regression Models for Correlated Failure Time Data. 10.3 Representation and
Estimation of the Bivariate Survivor Function. 10.4 Pairwise Dependency
Estimation. 10.5 Illustration: Australian Twin Data. 10.6 Approaches to
Nonparametric Estimation of the Bivariate Survivor Function. 10.7 Survivor
Function Estimation in Higher Dimensions. Bibliographic Notes. Exercises
and Complements. 11. Additional Failure Time Data Topics. 11.1
Introduction. 11.2 Stratified Bivariate Failure Time Analysis. 11.3 Fixed
Study Period Survival Studies. 11.4 Cohort Sampling and Case-Control
Studies. 11.5 Missing Covariate Data. 11.6 Mismeasured Covariate Data. 11.7
Sequential Testing with Failure Time Endpoints. 11.8 Bayesian Analysis of
the Proportional Hazards Model. 11.9 Some Analyses of a Particular Data
Set. Bibliographic Notes. Exercises and Complements. Glossary of Notation.
Appendix A: Some Sets of Data. Appendix B: Supporting Technical Material.
Bibliography. Author Index. Subject Index.
"...provides excellent exposure to the theory." ( Journal of Statistical Computation and Simulation , June 2005)
"The book contains a wealth of material and analytic insight...will continue to be an invaluable resource for all researchers and graduate students in the field...for years to come." ( Journal of the American Statistical Association , December 2003)
"...researchers in hazard function are likely to find new and valuable information in this book..." ( Journal of Mathematical Psychology , Vol. 47 2003)
"The book contains a wealth of material and analytic insight...will continue to be an invaluable resource for all researchers and graduate students in the field...for years to come." ( Journal of the American Statistical Association , December 2003)
"...researchers in hazard function are likely to find new and valuable information in this book..." ( Journal of Mathematical Psychology , Vol. 47 2003)