Estimating the mixture of a proportional hazards model with three types of failure data

Won Yun and Jong-Woon Kim

IISE Transactions, 2011, vol. 43, issue 11, 783-796

Abstract: Cox’s Proportional Hazards Model (PHM) has been widely applied in the analysis of lifetime data. It can be characterized in terms of covariates that influence the system lifetime, where the covariates describe the operating environment (e.g., temperature, pressure, humidity). When the covariates are assumed to be random variables, the hazards model becomes the mixed PHM. In this article, a parametric method is proposed to estimate the unknown parameters in the mixed PHM. Three types of data are considered: uncategorized field observations, categorized field ones, and categorized experimental ones. The expectation-maximization algorithm is used to handle the incomplete data problem. Simulation results are presented to illustrate the precision and some properties of the estimation results.Accepted in 2005 for a special issue on Reliability co-edited by Hoang Pham, Rutgers University; Dong Ho Park, Hallym University, Korea; and Richard Cassady, University of Arkansas.

Date: 2011
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/0740817X.2011.590436 (text/html)
Access to full text is restricted to subscribers.

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:taf:uiiexx:v:43:y:2011:i:11:p:783-796

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/uiie20

DOI: 10.1080/0740817X.2011.590436

Access Statistics for this article

IISE Transactions is currently edited by Jianjun Shi

More articles in IISE Transactions from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().