Analyzing step-stress accelerated life testing data using generalized linear models

Jinsuk Lee and Rong Pan

IISE Transactions, 2010, vol. 42, issue 8, 589-598

Abstract: In this article the parameter estimation method of Step-Stress Accelerated Life Testing (SSALT) model is discussed by utilizing techniques of Generalized Linear Model (GLM). A multiple progressive SSALT with exponential failure data and right censoring is analyzed. The likelihood function of the SSALT is treated as being a censoring variate with Poisson distribution and the life-stress relationship is defined by a log link function of a GLM. Both the maximum likelihood estimation and the Bayesian estimation of GLM parameters are discussed. The iteratively weighted least squares method is implemented to obtain the maximum likelihood estimation solution. The Bayesian estimation is derived by applying Jeffreys' non-informative prior and the Markov chain Monte Carlo method. Finally, a real industrial example is presented to demonstrate these estimation methods.

Date: 2010
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/07408170903459976 (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:42:y:2010:i:8:p:589-598

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

DOI: 10.1080/07408170903459976

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 ().