Smoothing Parameter Selection for Nonparametric Regression Using Smoothing Spline
Smoothing Parameter Selection for Nonparametric Regression Using Smoothing Spline
In this paper, the smoothing parameter selection problem has been examined in respect to a smoothing spline implementation in predicting nonparametric regression models. For this purpose, a simulation study has been performed by using a program written in MATLAB. The simulation study provides a comparison of the nine smoothing parameter selection methods. In this connection, 500 replications have been performed in simulation for sample sets with different sizes. Thus, the appropriate selection criteria are provided for a suitable smoothing parameter selection.
WOS: 000214937300008
- Muğla University Turkey
smoothing parameter, Generalized Cross Validation, Computational problems in statistics, Smoothing Parameter, Monte Carlo methods, smoothing spline, Approximations to statistical distributions (nonasymptotic), Probabilistic models, generic numerical methods in probability and statistics, generalized cross validation, Smoothing Spline, Nonparametric Regression, nonparametric regression, Nonparametric regression and quantile regression, Selection Criteria, selection criteria
smoothing parameter, Generalized Cross Validation, Computational problems in statistics, Smoothing Parameter, Monte Carlo methods, smoothing spline, Approximations to statistical distributions (nonasymptotic), Probabilistic models, generic numerical methods in probability and statistics, generalized cross validation, Smoothing Spline, Nonparametric Regression, nonparametric regression, Nonparametric regression and quantile regression, Selection Criteria, selection criteria
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