Mathematical Methods in Survival Analysis, Reliability and Quality of Life

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.
algorithm analysis application approximation assumed asymptotic baseline bias calculated Chapter chi-squared coefficient complete model component conditional consider contact tracing convergence copula correlation counting process Cox model defined denote density depends detected diabetes dimension disinhibited eating distribution function ecological exponential failure Figure France E-mail Gaussian hazard function hazard rate HIV-positive HrQoL hypothesis individual interval kernel latent classes latent regression latent trait latent variable likelihood estimator likelihood function linear LMR model log-likelihood Markov process Mathematical matrix maximum likelihood mean measurement Mesbah method Nikulin non-parametric observed obtained parameters partial likelihood patients probability procedure proposed random variable Rasch model Raschfit Raschfit-fast recode regression model response variables risk factor sampling plans scale score sensorial analysis simulated Skater Statistics stochastic stratum indicator sub-scale Survival Analysis survival function Table Theorem theory transition truncated values variance vector Weibull Weibull distribution within-area