Goodness-of-Fit Tests For The Weibull Distribution With Unknown Parameters And Heavy Censoring
Abstract
Goodness-of-fit tests are considered for testing the two-parameters Weibull distribution based on type II censored sampling with both parameters assumed unknown. Some extremely heavy censoring levels are considered which are useful when analyzing in-service field data with a large population and a small number of failures. Critical values are obtained by Monte Carlo simulation for Kolmogorov-Smirnov, Kuiper and Cramer-von Mises type test statistics. The approximate Snedecor's F-distribution is verified for the Mann-Scheuer-Fertig test statistic. The two-sided Mann-Scheuer-Fertig test is also studied and found to be important. A power study is carried out for these four test statistics for both moderate and heavy censoring for a number of different alternative models. © 1985, Taylor & Francis Group, LLC. All rights reserved.
Recommended Citation
M. Aho et al., "Goodness-of-Fit Tests For The Weibull Distribution With Unknown Parameters And Heavy Censoring," Journal of Statistical Computation and Simulation, vol. 21, no. 3 thru 4, pp. 213 - 225, Taylor and Francis Group; Taylor and Francis, Jan 1985.
The definitive version is available at https://doi.org/10.1080/00949658508810816
Department(s)
Mathematics and Statistics
Second Department
Geosciences and Geological and Petroleum Engineering
International Standard Serial Number (ISSN)
1563-5163; 0094-9655
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2023 Taylor and Francis Group; Taylor and Francis, All rights reserved.
Publication Date
01 Jan 1985
