Model selection with application to gamma process and inverse Gaussian process

Zhang, Mimi and Revie, Matthew; Walls, Lesley and Revie, Matthew and Bedford, Time, eds. (2016) Model selection with application to gamma process and inverse Gaussian process. In: Risk, Reliability and Safety. CRC/Taylor & Francis Group, London, UK. ISBN 9781138029972

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Abstract

The gamma process and the inverse Gaussian process are widely used in condition-based maintenance. Both are suitable for modelling monotonically increasing degradation processes. One challenge for practitioners is determining which of the two processes is most appropriate in light of a real data set. A common practice is to select the one with a larger maximized likelihood. However, due to variations in the data, the maximized likelihood of the “wrong” model could be larger than that of the “right” model. This paper proposes an efficient and broadly applicable test statistic for model selection. The construction of the test statistic is based on the Fisher information. Extensive numerical study is conducted to indicate the conditions under which the gamma process can be well approximated by the inverse Gaussian process, or the other way around.

ORCID iDs

Zhang, Mimi ORCID logoORCID: https://orcid.org/0000-0002-3807-297X and Revie, Matthew ORCID logoORCID: https://orcid.org/0000-0002-0130-8109; Walls, Lesley, Revie, Matthew and Bedford, Time
  • Item type:Book Section
    ID code:58769
    Dates:
    Date
    Event
    13 September 2016
    Published
    13 June 2016
    Accepted
    Notes:This is an Accepted Manuscript of a book chapter published by CRC Press in Risk, Reliability and Safety: Innovating Theory and Practice: Proceedings of ESREL 2016 (Glasgow, Scotland, 25-29 September 2016) on 13/09/2016, available : https://www.crcpress.com/Risk-Reliability-and-Safety-Innovating-Theory-and-Practice-Proceedings/Walls-Revie-Bedford/p/book/9781138029972
    Subjects:Social Sciences > Industries. Land use. Labor > Management. Industrial Management
    Department:Strathclyde Business School > Management Science
    Depositing user: Pure Administrator
    Date deposited:23 Nov 2016 14:27
    Last modified:11 Nov 2024 15:07
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/58769
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