Statistical Models and Methods for Lifetime Data

Praise for the First Edition
"An indispensable addition to any serious collection on lifetime data analysis and . . . a valuable contribution to the statistical literature. Highly recommended . . ."
–Choice
"This is an important book, which will appeal to statisticians working on survival analysis problems."
–Biometrics
"A thorough, unified treatment of statistical models and methods used in the analysis of lifetime data . . . this is a highly competent and agreeable statistical textbook."
–Statistics in Medicine
The statistical analysis of lifetime or response time data is a key tool in engineering, medicine, and many other scientific and technological areas. This book provides a unified treatment of the models and statistical methods used to analyze lifetime data.
Equally useful as a reference for individuals interested in the analysis of lifetime data and as a text for advanced students, Statistical Models and Methods for Lifetime Data, Second Edition provides broad coverage of the area without concentrating on any single field of application. Extensive illustrations and examples drawn from engineering and the biomedical sciences provide readers with a clear understanding of key concepts.
New and expanded coverage in this edition includes:
∗ Observation schemes for lifetime data
∗ Multiple failure modes
∗ Counting process–martingale tools
∗ Both special lifetime data and general optimization software
∗ Mixture models
∗ Treatment of interval–censored and truncated data
∗ Multivariate lifetimes and event history models
∗ Resampling and simulation methodology

Spis treści:
Basic Concepts and Models.
Observation Schemes, Censoring and Likelihood.
Some Nonparametric and Graphical Procedures.
Inference Procedures for Parametric Models.
Inference procedures for Log–Location–Scale Distributions.
Parametric Regression Models.
Semiparametric Multiplicative Hazards Regression Models.
Rank–Type and Other Semiparametric Procedures for Log–Location–Scale Models.
Multiple Modes of Failure.
Goodness of Fit Tests.
Beyond Univariate Survival Analysis.
Appendix A. Glossary of Notation and Abbreviations.
Appendix B. Asymptotic Variance Formulas, Gamma Functions and Order Statistics.
Appendix C. Large Sample Theory for Likelihood and Estimating Function Methods.
Appendix D. Computational Methods and Simulation.
Appendix E. Inference in Location–Scale Parameter Models.
Appendix F. Martingales and Counting Processes.
Appendix G. Data Sets.
References.

Nota biograficzna:
JERALD F. LAWLESS, PhD, is a professor and holder of the General Motors Canada–NSERC Industrial Research Chair in the Department of Statistics & Actuarial Science at the University of Waterloo, Ontario, Canada.

Okładka tylna:
Praise for the First Edition
"An indispensable addition to any serious collection on lifetime data analysis and . . . a valuable contribution to the statistical literature. Highly recommended . . ."
–Choice
"This is an important book, which will appeal to statisticians working on survival analysis problems."
–Biometrics
"A thorough, unified treatment of statistical models and methods used in the analysis of lifetime data . . . this is a highly competent and agreeable statistical textbook."
–Statistics in Medicine
The statistical analysis of lifetime or response time data is a key tool in engineering, medicine, and many other scientific and technological areas. This book provides a unified treatment of the models and statistical methods used to analyze lifetime data.
Equally useful as a reference for individuals interested in the analysis of lifetime data and as a text for advanced students, Statistical Models and Methods for Lifetim e Data, Second Edition provides broad coverage of the area without concentrating on any single field of application. Extensive illustrations and examples drawn from engineering and the biomedical sciences provide readers with a clear understanding of key concepts.
New and expanded coverage in this edition includes:
∗ Observation schemes for lifetime data
∗ Multiple failure modes
∗ Counting process–martingale tools
∗ Both special lifetime data and general optimization software
∗ Mixture models
∗ Treatment of interval–censored and truncated data
∗ Multivariate lifetimes and event history models
∗ Resampling and simulation methodology