Maximum likelihood estimation in a Weibull regression model with type-1 censoring: a Monte Carlo study
作者:
T Elperin,
I Gertsbakh,
期刊:
Communications in Statistics - Simulation and Computation
(Taylor Available online 1987)
卷期:
Volume 16,
issue 2
页码: 349-371
ISSN:0361-0918
年代: 1987
DOI:10.1080/03610918708812595
出版商: Marcel Dekker, Inc.
关键词: Parametric Regression;Point and Confidence Estimation;Normal Large-Sample Approximation
数据来源: Taylor
摘要:
Results of the Monte Carlo study of the performance of a maximum likelihood estimation in a Weibull parametric regression model with two explanatory variables are presented. One simulation run contained 1000 samples censored on the average by the amount of 0-30%. Each simulatedsample was generated in a form of two-factor two-level balanced experiment. The confidence intervals were computed using the large-sample normal approximation via the matrix of observed information. For small sample sizes the estimates of the scale parameter b of the loglifetime were significantly negatively biased, which resulted in a poor quality of confidence intervals for b and the low-level quantiles. All estimators improved their quality when the nominal value of b decreased. A moderate amount of censoring improved the quality of point and confidence estimation. The reparametrization b 7 produced rather accurate confidence intervals. Exact confidence intervals for b in case of non-censoring were obtained using the pivotal quantity b/b.
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