Estimation on Lomax Progressive Censoring Using the EM Algorithm
Journal of Statistical Computation and Simulation
Based on progressively type-II censored data, the maximum-likelihood estimators (MLEs) for the Lomax parameters are derived using the expectation–maximization (EM) algorithm. Moreover, the expected Fisher information matrix based on the missing value principle is computed. Using extensive simulation and three criteria, namely, bias, root mean squared error and Pitman closeness measures, we compare the performance of the MLEs via the EM algorithm and the Newton–Raphson (NR) method. It is concluded that the EM algorithm outperforms the NR method in all the cases. Two real data examples are used to illustrate our proposed estimators.
Helu, Amal, Hani Samawi, Mohammad Z. Raqab.
"Estimation on Lomax Progressive Censoring Using the EM Algorithm."
Journal of Statistical Computation and Simulation, 85 (5): 1035-1052.