62 research outputs found
Estimating Discrete Markov Models From Various Incomplete Data Schemes
The parameters of a discrete stationary Markov model are transition
probabilities between states. Traditionally, data consist in sequences of
observed states for a given number of individuals over the whole observation
period. In such a case, the estimation of transition probabilities is
straightforwardly made by counting one-step moves from a given state to
another. In many real-life problems, however, the inference is much more
difficult as state sequences are not fully observed, namely the state of each
individual is known only for some given values of the time variable. A review
of the problem is given, focusing on Monte Carlo Markov Chain (MCMC) algorithms
to perform Bayesian inference and evaluate posterior distributions of the
transition probabilities in this missing-data framework. Leaning on the
dependence between the rows of the transition matrix, an adaptive MCMC
mechanism accelerating the classical Metropolis-Hastings algorithm is then
proposed and empirically studied.Comment: 26 pages - preprint accepted in 20th February 2012 for publication in
Computational Statistics and Data Analysis (please cite the journal's paper
Optimum step-stress for temperature accelerated life test
International audienc
An inference method for temperature step-stress accelerated life testing
International audienc
Optimum step-stress for temperature accelerated life testing
International audienc
Modelling spread of diseases using a survival technique
International audienc
FAILURE RATE ESTIMATION IN A DYNAMIC ENVIRONMENT
International audienceWe present a method to assess the reliability of a material operating in a dynamic environment. The dynamic environment is represented as a sequence of shocks governed by a self-exciting point process. Different distribution models for the time-to-failure of the material are suggested. The case of a piecewise exponential model where a Cox's model is integrated to take into account the effect of the stress is investigated. Maximum likelihood estimates of the model parameters are studied
Estimation du taux de défaillance pour des équipements industriels sous contraintes d'environnement
International audienc
Fiabilité d'un système modélisé par un couple de processus aléatoires dépendants
International audienc
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