A new approach to estimate parameters of the two-parameter Weibull distribution

This study proposes a new approach to estimate the parameters of the two-parameter Weibull distribution in the case of complete (uncensored) or doubly Type II censored samples. The proposed estimators have closed forms and can be adapted to Type II right or Type II left censored samples. The new est...

Full description

Saved in:
Bibliographic Details
Published in:Statistics (Berlin, DDR) DDR), 2024-05, Vol.58 (3), p.621-664
Main Author: Karahasan, Mehmet
Format: Article
Language:English
Subjects:
Asymptotic methods
Bias
doubly Type II censoring
Efficiency
Goodness of fit
Maximum likelihood estimation
Method of moments
order statistics
Parameter estimation
Primary 62F10
Secondary 62G30, 62N05
Simulation
Statistical tests
Weibull distribution
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This study proposes a new approach to estimate the parameters of the two-parameter Weibull distribution in the case of complete (uncensored) or doubly Type II censored samples. The proposed estimators have closed forms and can be adapted to Type II right or Type II left censored samples. The new estimators are strongly consistent and the new shape estimator follows asymptotically a normal and the new scale estimator a lognormal distribution. Bias, mean square and efficiency performance of the new estimators are investigated through simulations and compared to some known estimation methods such as maximum likelihood estimation, method of moments, quantile estimation, maximum goodness of fit estimation, L-moments, U-statistics and bias-corrected maximum likelihood. According to the simulations, the newly proposed estimators seem to have good efficiency performance in comparison to the other methods, particularly in small samples. Furthermore, two real data sets are used to illustrate the new estimators.
ISSN:0233-1888
1029-4910
DOI:10.1080/02331888.2024.2353117