کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
415965 | 681266 | 2010 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Measuring the prediction error. A comparison of cross-validation, bootstrap and covariance penalty methods
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نظریه محاسباتی و ریاضیات
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چکیده انگلیسی
The estimators most widely used to evaluate the prediction error of a non-linear regression model are examined. An extensive simulation approach allowed the comparison of the performance of these estimators for different non-parametric methods, and with varying signal-to-noise ratio and sample size. Estimators based on resampling methods such as Leave-one-out, parametric and non-parametric Bootstrap, as well as repeated Cross Validation methods and Hold-out, were considered. The methods used are Regression Trees, Projection Pursuit Regression and Neural Networks. The repeated-corrected 10-fold Cross-Validation estimator and the Parametric Bootstrap estimator obtained the best performance in the simulations.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 54, Issue 12, 1 December 2010, Pages 2976–2989
Journal: Computational Statistics & Data Analysis - Volume 54, Issue 12, 1 December 2010, Pages 2976–2989
نویسندگان
Simone Borra, Agostino Di Ciaccio,