Maximum likelihood and Bayesian inference for common-cause of failure model

International audienc

Bayesian inference for Common cause failure rate based on causal inference with missing data

International audienc

Estimation from aggregate data

A statistical methodology to handle aggregate data is proposed. Aggregate data arise in many fields such as medical science, ecology, social science, reliability, etc. They can be described as follows: individuals are moving progressively along a finite set of states and observations are made in a time window split into several intervals. At each observation time, the only available information is the number of individuals in each state and the history of each item viewed as a stochastic process is thus lost. The time spent in a given state is unknown. Using a data completion technique, an estimation of the hazard rate in each state based on sojourn times is obtained and an estimation of the survival function is deduced. These methods are studied through simulations and applied to a data set. The simulation study shows that the algorithms involved in the methods converge and are robust.Aggregate data Missing data Survival function Hazard rate EM and MCEM algorithms Metropolis-Hastings algorithm

Planning step-stress test under Type-I censoring for the exponential case

Published online: 22 Oct 2012. NSC 99-2118-M-032-011-MY3[[abstract]]We consider in this work a k-level step-stress accelerated life-test (ALT) experiment with unequal duration steps τ=(τ1, …, τk). Censoring is allowed only at the change-stress point in the final stage. An exponential failure time distribution with mean life that is a log-linear function of stress, along with a cumulative exposure model, is considered as the working model. The problem of choosing the optimal τ is addressed using the variance-optimality criterion. Under this setting, we then show that the optimal k-level step-stress ALT model with unequal duration steps reduces just to a 2-level step-stress ALT model.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]SCI[[ispeerreviewed]]Y[[booktype]]紙本[[booktype]]電子版[[countrycodes]]GB

Planning step-stress test plans under Type-I censoring for the log-location-scale case

[[abstract]]In this paper, we consider a k-level step-stress accelerated life-testing (ALT) experiment with unequal duration steps τ=(τ1, …, τ k ). Censoring is allowed only at the change-stress point in the final stage. A general log-location-scale lifetime distribution with mean life which is a linear function of stress, along with a cumulative exposure model, is considered as the working model. Under this model, the determination of the optimal choice of τ for both Weibull and lognormal distributions are addressed using the variance–optimality criterion. Numerical results show that for a general log-location-scale distributions, the optimal k-step-stress ALT model with unequal duration steps reduces just to a 2-level step-stress ALT model.[[notice]]補正完畢[[journaltype]]國外[[booktype]]紙本[[countrycodes]]GB