Mathematical Methods in Survival... | Buy

Summary

Reliability and survival analysis are important applications of stochastic mathematics (probability, statistics and stochastic processes) that are usually covered separately in spite of the similarity of the involved mathematical theory. This title aims to redress this situation: it includes 21 chapters divided into four parts: Survival analysis, Reliability, Quality of life, and Related topics. Many of these chapters were presented at the European Seminar on Mathematical Methods for Survival Analysis, Reliability and Quality of Life in 2006.

Author Biography

Catherine Huber is an Emeritus professor at Université de Paris René Descartes. Her research activity concerns nonparametric and semi-parametric theory of statistics and their applications in biology and medicine. She has several publications in particular in the field of survival analysis. She is the co-author and co-editor of several books in the above fields.

Nikolaos Limnios is a professor at the University of Technology of Compiègne. His research and teaching activities concern stochastic processes, statistical inference and their applications in particular in reliability and survival analysis. He is the co-author and co-editor of several books in the above fields.

Mounir Mesbah is a professor at the Université Pierre et Marie Curie, Paris 6. His research and teaching activities concern statistics and its applications in health science and medicine (biostatistics). He is the co-author of several articles and co-editor of several books in the above fields.

Mikhail Nikulin is a professor at the Université Victor Segalen, and a member of the Institute of Mathematics at Bordeaux. His research and teaching activities concern mathematical statistics and its applications in reliability and survival analysis. He is the co-author and co-editor of several books in the above fields.

Part 1: Survival Analysis.

Chapter 1. Model selection for additive regression (E. Brunel and F. Comte).

Chapter 2. Nonparametric estimation of conditional probabilities, means and quantiles (O. Pons).

Chapter 3. Inference in transformation models for arbitrarily censored and truncated data (F. Vonta, C. Huber).

Chapter 4. Within-area distribution (L. Fortunato et al).

Chapter 5. Semi-Markov models in medicine (E. Mathieu-Dupas, C. Gras-Aygon, J-P. Daures).

Chapter 6. Bivariate Cox models (M. Broniatowski, A. Depire, Y. Ritov).

Chapter 7. A nonparametric estimation of a class of survival functionals
(B. Abdous).

Chapter 8. Approximate likelihood (H. Läuter).

Part 2: Reliability.

Chapter 9. Cox regression with missing values of a non-proportional covariate
(J-F. Dupuy, E. Leconte).

Chapter 10. Bayesian sampling plan for type-I censored exponential data
(C-T. Lin, Y-L. Huang, N. Balakrishnan).

Chapter 11. Reliability of stochastic dynamical systems applied to fatigue crack growth (J. Chiquet and N. Limnios).

Chapter 12. Statistical analysis of a redundant system with one stand-by unit
(V. Bagdonavicius and I. Masiulaityte, M. Nikulin).

Chapter 13. A modified test for the three-parameter Weibull distribution
(V. Voinov, R. Alloyarova and N. Pya).

Chapter 14. Accelerated life testing when the hazard rate function has cup shape (V. Bagdonavicius, L. Clerjaud, M. Nikulin).

Chapter 15. Software reliability (J. Ledoux).

Part 3: Quality of Life.

Chapter 16. Latent Markov Rasch model (F. Bartolucci, F. Pennoni, M. Lupparelli).

Chapter 17. Selection of items fitting a Rasch model (J-B. Hardouin, M. Mesbah).

Chapter 18. Longitudinal latent regression modeling (S. Bacci).

Chapter 19. Empirical validation of a quality of life instrument (J. Chwalow et al).

Part 4: Related Topics.

Chapter 20. Deterministic modeling of the size of the HIV/AIDS epidemic in Cuba (R. Lounes, H. de Arazoza, Y. H.Hsieh, J. Joanes).

Chapter 21. Some probabilistic models useful in sport sciences
(L. Gerville-Réache, M. Nikulin, S. Orazio, N. Paris, V. Rosa).

List of Authors.

Index.

Supplemental Materials

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