Journal of Multivariate Analysis
A limited memory algorithm for bound constrained optimization
SIAM Journal on Scientific Computing
Computational Statistics & Data Analysis
A new Bayes estimate of the change point in the hazard function
Computational Statistics & Data Analysis
Parametric estimation of change-points for actual event data in recurrent events models
Computational Statistics & Data Analysis
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Hazard function plays an important role in reliability and survival analysis. In some real life applications, abrupt changes in the hazard function may be observed and it is of interest to detect the location and the size of the change. Hazard models with a change-point are considered when the observations are subject to random left truncation and right censoring. For a piecewise constant hazard function with a single change-point, two estimation methods based on the maximum likelihood ideas are considered. The first method assumes parametric families of distributions for the censoring and truncation variables, whereas the second one is based on conditional likelihood approaches. A simulation study is carried out to illustrate the performances of the proposed estimators. The results indicate that the fully parametric method performs better especially for estimating the size of the change, however the difference between the two methods vanish as the sample size increases. It is also observed that the full likelihood approach is not robust to model misspecification.