Results 11 to 20 of about 448,585 (316)
Resistant selection of the smoothing parameter for smoothing splines
Statistics and Computing, 2001 Robust automatic selection techniques for the smoothing parameter of a smoothing spline are introduced. They are based on a robust predictive error criterion and can be viewed as robust versions of Cp and cross-validation. They lead to smoothing splines which are stable and reliable in terms of mean squared error over a large spectrum of model ...
Cantoni, Eva, Ronchetti, Elvezio
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New Bandwidth Selection for Kernel Quantile Estimators
Journal of Probability and Statistics, 2012 We propose a cross-validation method suitable for smoothing of kernel quantile estimators. In particular, our proposed method selects the bandwidth parameter, which is known to play a crucial role in kernel smoothing, based on unbiased estimation of a ...
Ali Al-Kenani, Keming Yu
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Direct Determination of Smoothing Parameter for Penalized Spline Regression [PDF]
Journal of Probability and Statistics, 2014 Penalized spline estimator is one of the useful smoothing methods. To construct the estimator, having goodness of fit and smoothness, the smoothing parameter should be appropriately selected. The purpose of this paper is to select the smoothing parameter
Takuma Yoshida
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A reliable data-based smoothing parameter selection method for circular kernel estimation [PDF]
Statistics and computing, 2022 A new data-based smoothing parameter for circular kernel density (and its derivatives) estimation is proposed. Following the plug-in ideas, unknown quantities on an optimal smoothing parameter are replaced by suitable estimates.
J. Ameijeiras‐Alonso
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Optimization Methods and Software, 2013 In this paper, we consider a class of optimal parameter selection problems with continuous inequality constraints. By introducing a smoothing parameter, we formulate a sequence of KKT Karush-Kuhn-Tucker systems of this problem and then transform it into a system of constrained nonlinear equations.
Feng, Z., Yiu, K., Teo, Kok Lay
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Smoothing parameter selection in Nadaraya-Watson kernel nonparametric regression using nature-inspired algorithm optimization [PDF]
المجلة العراقية للعلوم الاحصائية, 2020 In the context of Nadaraya-Watson kernel nonparametric regression, the curve estimation is fully depending on the smoothing parameter. At this point, the nature-inspired algorithms can be used as an alternative tool to find the optimal selection. In this
Zinah Basheer, Zakariya Algamal
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Choice of Smoothing Parameter for Kernel Type Ridge Estimators in Semiparametric Regression Models
Revstat Statistical Journal, 2021 This paper concerns kernel-type ridge estimators of parameters in a semiparametric model. These estimators are a generalization of the well-known Speckman’s approach based on kernel smoothing method. The most important factor in achieving this smoothing
Ersin Yilmaz +2 more
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Design-based spatial interpolation with data driven selection of the smoothing parameter
Environmental and Ecological Statistics, 2023 In the inverse distance weighting interpolation the interpolated, value is a weighted mean of the sampled values, with weights decreasing with the distances.
L. Fattorini +4 more
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Smoothing parameter selection in kernel nonparametric regression using bat optimization algorithm
, 2021 In the context of kernel nonparametric regression, the curve estimation is fully depending on the smoothing parameter. At this point, the nature-inspired algorithms can be used as an alternative tool to find the optimal selection.
Marwah Yahya Mustafa, Z. Algamal
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Stats, 2021 Functional data analysis techniques, such as penalized splines, have become common tools used in a variety of applied research settings. Penalized spline estimators are frequently used in applied research to estimate unknown functions from noisy data ...
Lauren N. Berry, Nathaniel E. Helwig
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