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El. knyga: Counting Processes and Survival Analysis [Wiley Online]

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The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.

"The book is a valuable completion of the literature in this field. It is written in an ambitious mathematical style and can be recommended to statisticians as well as biostatisticians."
-Biometrische Zeitschrift

"Not many books manage to combine convincingly topics from probability theory over mathematical statistics to applied statistics. This is one of them. The book has other strong points to recommend it: it is written with meticulous care, in a lucid style, general results being illustrated by examples from statistical theory and practice, and a bunch of exercises serve to further elucidate and elaborate on the text."
-Mathematical Reviews

"This book gives a thorough introduction to martingale and counting process methods in survival analysis thereby filling a gap in the literature."
-Zentralblatt f?r Mathematik und ihre Grenzgebiete/Mathematics Abstracts

"The authors have performed a valuable service to researchers in providing this material in [ a] self-contained and accessible form. . . This text [ is] essential reading for the probabilist or mathematical statistician working in the area of survival analysis."
-Short Book Reviews, International Statistical Institute

Counting Processes and Survival Analysis explores the martingale approach to the statistical analysis of counting processes, with an emphasis on the application of those methods to censored failure time data. This approach has proven remarkably successful in yielding results about statistical methods for many problems arising in censored data. A thorough treatment of the calculus of martingales as well as the most important applications of these methods to censored data is offered. Additionally, the book examines classical problems in asymptotic distribution theory for counting process methods and newer methods for graphical analysis and diagnostics of censored data. Exercises are included to provide practice in applying martingale methods and insight into the calculus itself.
Preface vii
The Applied Setting
1(14)
Introduction
1(1)
A Data Set and Some Examples
2(13)
The Counting Process and Martingale Framework
15(36)
Introduction
15(1)
Stochastic Processes and Stochastic Integrals
15(10)
The Martingale M = N - A
25(6)
The Doob-Meyer Decomposition: Applications to Quadratic Variation
31(11)
The Martingale Transform ∫HdM
42(6)
Bibliographic Notes
48(3)
Local Square Integrable Martingales
51(38)
Introduction
51(1)
Localization of Stochastic Processes and the Doob-Meyer Decomposition
52(8)
The Martingale N - A Revisited
60(5)
Stochastic Integrals with Respect to Local Martingales
65(9)
Continuous Compensators
74(5)
Compensators with Discontinuities
79(4)
Summary
83(5)
Bibliographic Notes
88(1)
Finite Sample Moments and Large Sample Consistency of Tests and Estimators
89(36)
Introduction
89(2)
Nonparametric Estimation of the Survival Distribution
91(16)
Some Finite Sample Properties of Linear Rank Statistics
107(5)
Consistency of the Kaplan-Meier Estimator
112(9)
Bibliographic Notes
121(4)
Censored Data Regression Models and Their Application
125(76)
Introduction
125(1)
The Proportional Hazards and Multiplicative Intensity Models
126(10)
Partial Likelihood Inference
136(17)
Applications of Partial Likelihood Methods
153(10)
Martingale Residuals
163(15)
Applications of Residual Methods
178(19)
Bibliographic Notes
197(4)
Martingale Central Limit Theorem
201(28)
Preliminaries and Motivation
201(4)
Convergence of Martingale Difference Arrays
205(10)
Weak Convergence of the Process, U(n)
215(13)
Bibliographic Notes
228(1)
Large Sample Results of the Kaplan-Meier Estimator
229(26)
Introduction
229(1)
A Large Sample Result for Kaplan-Meier and Weighted Logrank Statistics
229(6)
Confidence Bands for the Survival Distribution
235(17)
Bibliographic Notes
252(3)
Weighted Logrank Statistics
255(32)
Introduction
255(1)
Large Sample Null Distribution
256(9)
Consistency of Tests of the Class K
265(2)
Efficiencies of Tests of the Class K
267(10)
Some Versatile Test Procedures
277(7)
Bibliographic Notes
284(3)
Distribution Theory for Proportional Hazards Regression
287(30)
Introduction
287(2)
The Partial Likelihood Score Statistic
289(7)
Estimators of the Regression Parameters and the Cumulative Hazard Function
296(7)
The Asymptotic Theory for Simple Models
303(8)
Asymptotic Relative Efficiency of Partial Likelihood Inference in the Proportional Hazards Model
311(5)
Bibliographic Notes
316(1)
Appendix A. Some Results from Stieltjes Integration and Probability Theory 317(14)
Appendix B. An Introduction to Weak Convergence 331(12)
Appendix C. The Martingale Central Limit Theorem: Some Preliminaries 343(16)
Appendix D. Data 359(26)
Appendix E. Exercises 385(16)
Bibliography 401(12)
Notation 413(4)
Author Index 417(4)
Subject Index 421