Exact Inference for Competing Risks Model with Generalized Progressive Hybrid Censored Exponential Data

In this paper, we derive exact likelihood inference for competing risks model (CRM) with generalized progressive hybrid censored exponential data. Based on the maximum likelihood estimators (MLEs) for unknown parameters, we derive the conditional moment generating function (MGF). Also, based on the first moments of the MLEs, we provide the bias adjusted estimators. We compare the proposed estimators by Monte Carlo simulation. For fixed sample and progressive censored sample size, the MSEs decrease as the pre-fixed time increases. For pre-fixed time, sample and progressive censored sample size, the MSEs decrease as the number of guarantee sample size increases. Also, we found that the bias adjusted estimators are better in terms of bias and MSE. But, we found that MLEs are better than the bias adjusted estimators in terms of variances. The simulation step is repeated 10,000 times for the  = 20, 30, and 40 and various generalized progressive hybrid censoring scheme (GPHCS).