کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
417449 681519 2013 15 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Cross Validation and Maximum Likelihood estimations of hyper-parameters of Gaussian processes with model misspecification
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
پیش نمایش صفحه اول مقاله
Cross Validation and Maximum Likelihood estimations of hyper-parameters of Gaussian processes with model misspecification
چکیده انگلیسی

The Maximum Likelihood (ML) and Cross Validation (CV) methods for estimating covariance hyper-parameters are compared, in the context of Kriging with a misspecified covariance structure. A two-step approach is used. First, the case of the estimation of a single variance hyper-parameter is addressed, for which the fixed correlation function is misspecified. A predictive variance based quality criterion is introduced and a closed-form expression of this criterion is derived. It is shown that when the correlation function is misspecified, the CV does better compared to ML, while ML is optimal when the model is well-specified. In the second step, the results of the first step are extended to the case when the hyper-parameters of the correlation function are also estimated from data.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 66, October 2013, Pages 55–69
نویسندگان
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