Examining the identifiability and estimability of the phase-type ageing model

Boquan Cheng and Rogemar Mamon ()
Additional contact information
Boquan Cheng: The University of Western Ontario
Rogemar Mamon: The University of Western Ontario

Computational Statistics, 2024, vol. 39, issue 2, No 23, 963-1004

Abstract: Abstract In this paper, the identifiability and the estimability of a particular phase-type ageing model (PTAM) are investigated. We consider a PTAM that is shown to be identifiable, although it has poor estimability when the only observation is the time until absorption. We use a data-cloning method to assess model estimability, which is also analysed visually via contour plots and marginal likelihood functions. The PTAM’s estimability under different scenarios is compared, from the best scenario of a fully observable Markov process for each individual to the worst scenario wherein the only observation is the time until absorption for each individual. Some in-between scenarios are also studied and include the situations where the state could be observed every several years and the state could be measured with some error. Certain conditions are provided regarding the state of the Markov chain that improves the PTAM’s estimability.

Keywords: Phase-type ageing model; Identifiability; Estimability; Data cloning (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00180-023-01329-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:39:y:2024:i:2:d:10.1007_s00180-023-01329-5

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s00180-023-01329-5

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().