On computing multiple change points for the gamma distribution

Journal Article (2021)
Authors

Xun Xiao (Massey University)

P. Chen (Institute of High Performance Computing)

Zhi Sheng Ye (National University of Singapore)

Kwok Leung Tsui (City University of Hong Kong)

Affiliation
External organisation
To reference this document use:
https://doi.org/10.1080/00224065.2020.1717398
More Info
expand_more
Publication Year
2021
Language
English
Affiliation
External organisation
Issue number
3
Volume number
53
Pages (from-to)
267-288
DOI:
https://doi.org/10.1080/00224065.2020.1717398

Abstract

This study proposes an efficient approach to detect one or more change points for gamma distribution. We plug a closed-form estimator into the gamma log-likelihood function to obtain a sharp approximation to the maximum of log-likelihood. We further derive a closed form calibration of approximate likelihood which is asymptotically equivalent to the exact log-likelihood. This circumvents iterative optimization procedures to find maximum likelihood estimates which can be a burden in detecting multiple change points. The simulation study shows that the approximation is accurate and the change points can be detected much faster. Two case studies on the time between events arising from industrial accidents are presented and extensively investigated.

No files available

Metadata only record. There are no files for this record.