TY - JOUR

T1 - Estimating the Burr XII parameters in constant-stress partially accelerated life tests under multiple censored data

AU - Cheng, Yung Fu

AU - Wang, Fu Kwun

PY - 2012

Y1 - 2012

N2 - In this article, we present the performance of the maximum likelihood estimates of the Burr XII parameters for constant-stress partially accelerated life tests under multiple censored data. Two maximum likelihood estimation methods are considered. One method is based on observed-data likelihood function and the maximum likelihood estimates are obtained by using the quasi-Newton algorithm. The other method is based on complete-data likelihood function and the maximum likelihood estimates are derived by using the expectation- maximization (EM) algorithm. The variance-covariance matrices are derived to construct the confidence intervals of the parameters. The performance of these two algorithms is compared with each other by a simulation study. The simulation results show that the maximum likelihood estimation via the EM algorithm outperforms the quasi-Newton algorithm in terms of the absolute relative bias, the bias, the root mean square error and the coverage rate. Finally, a numerical example is given to illustrate the performance of the proposed methods.

AB - In this article, we present the performance of the maximum likelihood estimates of the Burr XII parameters for constant-stress partially accelerated life tests under multiple censored data. Two maximum likelihood estimation methods are considered. One method is based on observed-data likelihood function and the maximum likelihood estimates are obtained by using the quasi-Newton algorithm. The other method is based on complete-data likelihood function and the maximum likelihood estimates are derived by using the expectation- maximization (EM) algorithm. The variance-covariance matrices are derived to construct the confidence intervals of the parameters. The performance of these two algorithms is compared with each other by a simulation study. The simulation results show that the maximum likelihood estimation via the EM algorithm outperforms the quasi-Newton algorithm in terms of the absolute relative bias, the bias, the root mean square error and the coverage rate. Finally, a numerical example is given to illustrate the performance of the proposed methods.

KW - Burr XII distribution

KW - EM algorithm

KW - Partially accelerated life test

KW - Quasi-Newton algorithm

UR - http://www.scopus.com/inward/record.url?scp=84862893710&partnerID=8YFLogxK

U2 - 10.1080/03610918.2011.617478

DO - 10.1080/03610918.2011.617478

M3 - 文章

AN - SCOPUS:84862893710

SN - 0361-0918

VL - 41

SP - 1711

EP - 1727

JO - Communications in Statistics: Simulation and Computation

JF - Communications in Statistics: Simulation and Computation

IS - 9

ER -