Document Type
Article
Publication Date
8-12-2024
Publication Title
Applied Stochastic Models in Business and Industry
Volume
41
Issue
3
First Page
e2886
DOI
https://doi.org/10.1002/asmb.2886
Abstract
The gamma process is widely used for the lifetime estimation of highly reliable products that degrade over time. Typically, incomplete likelihood is used to estimate the model parameters and the reliability estimates for the first passage time distribution of the gamma process; however, it (i.e., pseudo method) does not consider interval censoring and right censoring information of the degradation data. In this work, the expectation-maximization algorithm-based method (EM method) is developed for the estimation of the gamma process model parameters and the reliability estimates incorporating interval censoring and right censoring. The asymptotic variance–covariance matrix and the asymptotic confidence intervals for the parameters are constructed, and then a comparison between the pseudo method and the EM method is made. Monte Carlo simulation studies and real-life data applications are conducted in order to illustrate the performance of the proposed EM method over the pseudo method.
Recommended Citation
Palayangoda, Lochana and Balakrishnan, N., "An EM-based likelihood inference for degradation data analysis using gamma process" (2024). Mathematics Faculty Publications. 86.
https://digitalcommons.unomaha.edu/mathfacpub/86
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