On bayesian estimation of the multiple decrement function in the competing risks problem

Andrew A. Neath and Francisco J. Samaniego

Statistics & Probability Letters, 1996, vol. 31, issue 2, 75-83

Abstract: Classical methods are inapplicable in estimation problems involving non-identifiable parameters. Bayesian methods, on the other hand, are often both feasible and intuitively reasonable in such problems. This paper establishes the foundations for studying the efficacy of Bayesian updating in estimating nonidentifiable parameters in the competing risks framework. We obtain a useful representation of the posterior distribution of the multiple decrement function, assuming a Dirichler process prior, and derive the limiting posterior distribution. It is noted that posterior estimates of a nonidentifiable parameter may be inferior to estimates based on the prior distribution alone, even when the size of the available sample grows to infinity. This leads, among other things, to the search for distinguished parameter values, or models, in which Bayesian updating necessarily improves upon one's prior estimate. In a companion paper, it is shown that the multivariate exponential distribution can play such a role in the competing risks framework.

Keywords: Nonidentifiability; Efficacy; Survival; function; Bayesian; updating; Dirichlet; process; Multivariate; exponential; distribution (search for similar items in EconPapers)
Date: 1996
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167-7152(96)00016-8
Full text for ScienceDirect subscribers only

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:eee:stapro:v:31:y:1996:i:2:p:75-83

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

Access Statistics for this article

Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul

More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().