On some goodness-of-fit tests and their connection to graphical methods with uncensored and censored data

Claudia Castro-Kuriss, Mauricio Huerta, VICTOR ELISEO LEIVA SANCHEZ, Alejandra Tapia

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

In this work, we present goodness-of-fit tests related to the Kolmogorov-Smirnov and Michael statistics and connect them to graphical methods with uncensored and censored data. The Anderson-Darling test is often empirically more powerful than the Kolmogorov-Smirnov test. However, the former one cannot be related to graphical tools by means of probability plots, as the Kolmogorov-Smirnov test does. The Michael test is, in some cases, more powerful than the Anderson-Darling and Kolmogorov-Smirnov tests and can also be related to probability plots. We consider the Kolmogorov-Smirnov and Michael tests for detecting whether any distribution is suitable or not to model censored or uncensored data. We conduct numerical studies to show the performance of these tests and the corresponding graphical tools. Some comments related to big data and lifetime analysis, under the context of this study, are provided in the conclusions of this work.

Original languageEnglish
Title of host publicationProceedings of the 13th International Conference on Management Science and Engineering Management, 2019 - Volume 1
EditorsJiuping Xu, Gheorghe Duca, Fang Lee Cooke, Syed Ejaz Ahmed
PublisherSpringer Verlag
Pages157-183
Number of pages27
ISBN (Print)9783030212476
DOIs
StatePublished - 1 Jan 2020
Event13th International Conference on Management Science and Engineering Management, ICMSEM 2019 - St. Catharines, Canada
Duration: 5 Aug 20198 Aug 2019

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1001
ISSN (Print)2194-5357

Conference

Conference13th International Conference on Management Science and Engineering Management, ICMSEM 2019
Country/TerritoryCanada
CitySt. Catharines
Period5/08/198/08/19

Keywords

  • Anderson-Darling Kolmogorov-Smirnov and Michael tests
  • Big data
  • Censored data
  • Test power

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