TY - JOUR
T1 - Estimation on Lomax Progressive Censoring Using the EM Algorithm
AU - Helu, Amal
AU - Samawi, Hani
AU - Raqab, Mohammad Z.
N1 - Publisher Copyright:
© 2013, Taylor & Francis.
PY - 2015/1/1
Y1 - 2015/1/1
N2 - 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.
AB - 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.
KW - EM algorithm
KW - Lomax distribution
KW - Maximum-likelihood estimator
KW - Newton-Raphson algorithm
KW - Pittman closeness measure
KW - Progressive type-II censoring
UR - https://digitalcommons.georgiasouthern.edu/biostat-facpubs/90
UR - http://dx.doi.org/10.1080/00949655.2013.861837
U2 - 10.1080/00949655.2013.861837
DO - 10.1080/00949655.2013.861837
M3 - Article
SN - 0094-9655
VL - 85
JO - Journal of Statistical Computation and Simulation
JF - Journal of Statistical Computation and Simulation
ER -