دانلود مقاله ISI انگلیسی شماره 25949
عنوان فارسی مقاله

روش های هموارسازی پیش بینی کننده چندگانه برای تجزیه و تحلیل حساسیت: نتایج نمونه

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
25949 2008 23 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Multiple predictor smoothing methods for sensitivity analysis: Example results
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Reliability Engineering & System Safety, Volume 93, Issue 1, January 2008, Pages 55–77

کلمات کلیدی
مدل های افزودنی - عدم قطعیت معرفت شناختی - رگرسیون به صورت محلی وزن - رگرسیون ناپارامتری - رگرسیون پیگیری پروجکشن - رگرسیون پارتیشن بندی بازگشتی - تجزیه و تحلیل حساسیت - انتخاب گام به گام - تجزیه و تحلیل عدم قطعیت
پیش نمایش مقاله
پیش نمایش مقاله روش های هموارسازی پیش بینی کننده چندگانه برای تجزیه و تحلیل حساسیت: نتایج نمونه

چکیده انگلیسی

The use of multiple predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described in the first part of this presentation: (i) locally weighted regression (LOESS), (ii) additive models, (iii) projection pursuit regression, and (iv) recursive partitioning regression. In this, the second and concluding part of the presentation, the indicated procedures are illustrated with both simple test problems and results from a performance assessment for a radioactive waste disposal facility (i.e., the Waste Isolation Pilot Plant). As shown by the example illustrations, the use of smoothing procedures based on nonparametric regression techniques can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or quadratic regression when nonlinear relationships between model inputs and model predictions are present.

مقدمه انگلیسی

The first part of this presentation [1] reviews parametric and nonparametric regression procedures for use in sampling-based sensitivity analyses. Specifically, the following parametric regression procedures are introduced and briefly described in Section 2 of Ref. [1]: (i) linear regression (LIN_REG), (ii) rank regression (RANK_REG), and (iii) quadratic regression (QUAD_REG). Further, the following nonparametric regression procedures are introduced and briefly described in Section 3.3 of Ref. [1]: (i) locally weighted regression (LOESS), (ii) additive models (GAMs), (iii) projection pursuit regression (PP_REG), and (iv) recursive partitioning regression (RP_REG). In addition, algorithms for the stepwise implementation of these procedures in the R language as part of a sensitivity analysis are described in Section 4 of Ref. [1]. The efficacy of the various methods described in Ref. [1] as procedures for sensitivity analysis is now investigated with both analytic test model data and real data. The analytic test models were assembled as part of a review volume on sensitivity analysis [2] and [3]. The real data comes from a performance assessment for the Waste Isolation Pilot Plant (WIPP) [4] and [5]. The methods are compared on the basis of fidelity to the data, overfitting of the data, and reproducibility. The presentation is organized as follows. First, certain quantities used in assessing the efficacy of the various sensitivity analysis procedures are introduced (Section 2). Then, the results obtained with the analytic test models are presented (Section 3). Next, the results obtained with the data from the WIPP performance assessment are presented (Section 4). The presentation then ends with a summary of observations and insights (Section 5).

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پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.