Improved Tolerance Limits by Combining Analytical and Experimental Data: An Information Integration Methodology

We propose a coherent methodology for integrating different sources of information on a response variable of interest, in order to accurately predict percentiles of its distribution. Under the assumption that one of the sources is more reliable than the other(s), the approach combines factors formed from the data into an additive linear regression model. Quantile regression, designed for quantifying the goodness of fit precisely at a desired quantile, is used as the optimality criterion in model-fitting. Asymptotic confidence interval construction methods for the percentiles are adopted to compute statistical tolerance limits for the response. The approach is demonstrated on a materials science case study that pools together information on failure load from physical tests and computer model predictions. A small simulation study assesses the precision of the inferences. The methodology gives plausible percentile estimates. Resulting tolerance limits are close to nominal coverage probability levels.

並列關鍵字

A basis ； B basis ； quantile regression ； reliability

被引用紀錄

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鍾芳樺（2006）。國家與法作為人民的自我組織：論威瑪時代Hans Kelsen, Carl Schmitt與Hermann Heller對法最終證立問題的分析〔博士論文，國立臺灣大學〕。華藝線上圖書館。https://doi.org/10.6342/NTU.2006.01554

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延伸閱讀

周冠宇（2014）。An Empirical Study of Integrating Independent Component Analysis and Combined Forecasting for Time Series Data〔碩士論文，國立宜蘭大學〕。華藝線上圖書館。https://doi.org/10.6820/NIU.2014.00172
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國際替代計量

Improved Tolerance Limits by Combining Analytical and Experimental Data: An Information Integration Methodology

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