Publication Type
Journal Article
Version
submittedVersion
Publication Date
5-2017
Abstract
A general procedure is developed for bias-correcting the maximum likelihood estimators (MLEs) of the parameters of Weibull regression model with either complete or right-censored data. Following the bias correction, variance corrections and hence improved t-ratios for model parameters are presented. Potentially improved t-ratios for other reliability-related quantities are also discussed. Simulation results show that the proposed method is effective in correcting the bias of the MLEs, and the resulted t-ratios generally improve over the regular t-ratios.
Keywords
Bias correction, variance correction, bootstrap, improved t-ratios, stochastic expansion, right censoring
Discipline
Econometrics
Research Areas
Econometrics
Publication
Journal of Statistical Computation and Simulation
Volume
87
Issue
12
First Page
2349
Last Page
2371
ISSN
0094-9655
Identifier
10.1080/00949655.2017.1331441
Publisher
Taylor & Francis: STM, Behavioural Science and Public Health Titles
Citation
SHEN, Yan and YANG, Zhenlin.
Improved likelihood inferences for Weibull regression model. (2017). Journal of Statistical Computation and Simulation. 87, (12), 2349-2371.
Available at: https://ink.library.smu.edu.sg/soe_research/2080
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1080/00949655.2017.1331441