Correction

IF 1.3 4区 工程技术 Q4 ENGINEERING, INDUSTRIAL Quality Engineering Pub Date : 2023-01-02 DOI:10.1080/08982112.2023.2176110
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Abstract

An error was noted in R programming that caused a modest overestimation of variance component values associated with the oven and batch (block) random effects. This led to posterior predictive distributions for that were slightly too dispersed for the diameter and strength responses. This error came from inadvertently treating the posterior Stan scale parameter variables, SO_1, SO_2, SB_1, SBx2_1, SB_2, and SBx2_2 (shown in the Appendix), as variance (for normal) and variance-related (for student t) parameters, rather than as standard deviation (for normal) and standard-deviation-related (for student t) parameters. (Stan represents the scalevariation parameter for the normal distribution as standard deviation parameter, not a variance parameter. A similar result holds proportionately for the student t distribution in Stan.). The author has corrected this programing error and recomputed Figures 3, 4, 6, and 7 and Table 3. These are shown below. The other figures and tables were not affected. As a high-level validation, a separate, parallel calculation was done using Stan’s generated quantities feature to sample the posterior random effects directly from Stan. This produced results very similar to the original approach (but not the same due to Monte Carlo random variation).
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校正
在R编程中注意到一个错误,该错误导致对与烘箱和批次(块)随机效应相关的方差分量值的适度高估。这导致了对于直径和强度响应来说稍微过于分散的后验预测分布。该误差源于无意中将后验Stan量表参数变量SO_1、SO_2、SB_1、SBx2_1、SB_2和SBx2_2(如附录所示)视为方差(对于正态)和方差相关(对于学生t)参数,而不是标准差(对于正常)和标准差相关(对于学生t)参数。(Stan将正态分布的标度变化参数表示为标准差参数,而不是方差参数。Stan中的学生t分布也有类似的结果。)。作者纠正了这个编程错误,并重新计算了图3、4、6和7以及表3。如下所示。其他数字和表格没有受到影响。作为高级验证,使用Stan的生成量特征进行了单独的并行计算,以直接从Stan采样后验随机效应。这产生了与原始方法非常相似的结果(但由于蒙特卡罗随机变化而不同)。
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来源期刊
Quality Engineering
Quality Engineering ENGINEERING, INDUSTRIAL-STATISTICS & PROBABILITY
CiteScore
3.90
自引率
10.00%
发文量
52
审稿时长
>12 weeks
期刊介绍: Quality Engineering aims to promote a rich exchange among the quality engineering community by publishing papers that describe new engineering methods ready for immediate industrial application or examples of techniques uniquely employed. You are invited to submit manuscripts and application experiences that explore: Experimental engineering design and analysis Measurement system analysis in engineering Engineering process modelling Product and process optimization in engineering Quality control and process monitoring in engineering Engineering regression Reliability in engineering Response surface methodology in engineering Robust engineering parameter design Six Sigma method enhancement in engineering Statistical engineering Engineering test and evaluation techniques.
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