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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/20603


    Title: Reliability analysis using the least squares method in nonlinear mixed-effect degradation models
    Authors: Wu, S. J.;Shao, J.
    Contributors: 淡江大學統計學系
    Keywords: Asymptotic covariance matrix;Asymptotic normality;Consistency;Failure time distribution;Percentile
    Date: 1999-07
    Issue Date: 2013-08-08 14:48:18 (UTC+8)
    Publisher: Taipei: Academia Sinica * Institute of Statistical Science
    Abstract: We develop statistical inference procedures in assessing product reliability based on a nonlinear mixed-effect degradation model and the least squares method. With today's high technology, some life tests result in no or very few failures by the end of test. Thus, it is hard to use the traditional reliability analysis to analyze lifetime data. Since product performance degrades over time, we analyze the degradation data and use the analytical results to estimate percentiles of the failure time distribution. The nonlinear mixed-effect degradation model provides us a way to build the relationship between degradation measurements and time. We establish asymptotic properties of the ordinary and weighted least squares estimators under the nonlinear mixed-effect model. We use these asymptotic results to obtain point estimates and approximate confidence intervals for percentiles of the failure time distribution. Two real data sets are analyzed. Performances of the proposed method are studied by simulation.
    Relation: Statistica Sinica 9(3), pp.855-877
    Appears in Collections:[統計學系暨研究所] 期刊論文

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