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Failure Rate Analysis Using GLIMMIX

机译:使用Glimmix的故障率分析

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摘要

GLIMMIX is a SAS macro that provides analysis of data modeled by a generalized linear mixed model, GLMM, based on an approach in Wolfinger and O'Connell (1993) for maximizing the associated likelihood. We use this computing tool in the special case of Poisson failure counts for data on catastrophic time-dependent pump failures obtained from Martz and Whiteman (1984). The original analysis of this data, assuming a GLMM, was performed using Failure Rate Analysis code (FRAC), a code developed at Los Alamos National Laboratory to perform an analysis of variance of failure rates as described in Martz, Beckman and McInteer (1982). The FRAC approach to estimating the effects in a GLMM is basically fitting a linear mixed model to a transformed response variable. The GLIMMIX and FRAC approaches to GLMM estimation are described , delineating differences. The analysis results of these two computational approaches are compared for the pump failure data.
机译:Glimmix是一种SAS宏,它提供了通过广义线性混合模型,基于Wolfinger和O'Connell(1993)的方法来提供广义线性混合模型,GLMM的分析,以最大化相关的可能性。我们在从Martz和Whiteman(1984)获得的灾难性时间依赖泵故障的特殊情况下使用该计算工具。使用故障率分析代码(FRAC)进行了本数据的原始分析,假设GLMM,在Los Alamos国家实验室开发的代码,以分析Martz,Beckman和Mcinteer(1982)中所述的失败率的差异分析。估计GLMM效果的FRAC方法基本上将线性混合模型与变换的响应变量基本上拟合。描述了GLMM估计的GLIMMIX和FRAC方法,描绘了差异。对泵故障数据进行比较这两种计算方法的分析结果。

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