کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
491700 720193 2016 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Estimating the variance of the predictor in stochastic Kriging
ترجمه فارسی عنوان
برآورد واریانس پیش بینی کننده در کریجینگ تصادفی
کلمات کلیدی
کریجینگ؛ فرآیند گاوسی؛ واریانس پیش بینی؛ پلاگین خود راه انداز
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر علوم کامپیوتر (عمومی)
چکیده انگلیسی


• In practice, the parameters of a Kriging metamodel must be estimated.
• In practice, these estimated parameters are plugged into the predictor formula.
• This plugging-in creates bias in the estimator of the predictor variance.
• We present three methods for estimation of the predictor variance.
• We compare these methods through experiments with M/M/1 simulation model.

We study the estimation of the true variance of the predictor in stochastic Kriging (SK). First, we obtain macroreplications for a SK metamodel that approximates a single-server simulation model; these macroreplications give independently and identically distributed predictions. This simulation may use common random numbers (CRN). From these macroreplications we conclude that the usual plug-in estimator of the variance significantly underestimates the true variance. Because macroreplications of practical simulation models are computationally expensive, we next formulate two bootstrap methods that use a single macroreplication: (i) a distribution-free method that resamples simulation replications (within the single macroreplication), and (ii) a parametric method that assumes a Gaussian distribution for the SK predictor, and estimates the (hyper)parameters of that distribution from the single macroreplication. Altogether we recommend distribution-free bootstrapping for the estimation of the SK predictor variance in practical simulation experiments.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Simulation Modelling Practice and Theory - Volume 66, August 2016, Pages 166–173
نویسندگان
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