Biostatistics: Faculty Publications

Estimation on Lomax Progressive Censoring Using the EM Algorithm

Document Type

Article

Publication Date

2015

Publication Title

Journal of Statistical Computation and Simulation

DOI

10.1080/00949655.2013.861837

Abstract

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.

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