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A generalized linear model approach to designing accelerated life test experiments
Author(s) -
Monroe Eric M.,
Pan Rong,
AndersonCook Christine M.,
Montgomery Douglas C.,
Borror Connie M.
Publication year - 2011
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.1143
Subject(s) - censoring (clinical trials) , mathematical optimization , computer science , optimal design , exponential function , design of experiments , key (lock) , nonlinear system , variance (accounting) , fisher information , linear model , generalized linear model , mathematics , algorithm , statistics , machine learning , mathematical analysis , physics , computer security , accounting , quantum mechanics , business
Abstract Optimal experimental design practices are prominent in many applications. This paper proposes an alternate way of computing the information matrix, a key consideration in planning an accelerated life test. The generalized linear model approach allows optimal designs to be computed using iteratively weighted least‐square solutions versus a maximum likelihood method. This approach is demonstrated with an assumed exponential distribution and allows the practitioner to observe the underlying structure of the optimal experimental design matrix and its relationship to important factors such as censoring and a nonlinear response function. Optimality criteria are discussed for both parameter estimation and prediction variance at an intended usage condition, which is typically outside the feasible accelerated test region. Copyright © 2010 John Wiley & Sons, Ltd.