Is \(C_{p}\) an empirical Bayes method for smoothing parameter choice?

DOI10.1016/J.SPL.2003.06.001zbMath1048.62009OpenAlexW2135673192MaRDI QIDQ1423096

S. C. Kou

Publication date: 14 February 2004

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2003.06.001

zbMATH Keywords

Smoothing splineExponential familyMLEAICBICInverse Gaussian distributionGeneralized maximum likelihoodSURE

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) General nonlinear regression (62J02) Empirical decision procedures; empirical Bayes procedures (62C12)

Cites Work

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• Linear smoothers and additive models
• A comparison of generalized cross validation and modified maximum likelihood for estimating the parameters of a stochastic process
• A comparison of GCV and GML for choosing the smoothing parameter in the generalized spline smoothing problem
• Asymptotic optimality of \(C_ L\) and generalized cross-validation in ridge regression with application to spline smoothing
• Asymptotic optimality for \(C_ p\), \(C_ L\), cross-validation and generalized cross-validation: Discrete index set
• On the efficiency of selection criteria in spline regression
• Selection criteria for scatterplot smoothers
• The Signal Extraction Approach to Nonlinear Regression and Spline Smoothing
• How Biased is the Apparent Error Rate of a Prediction Rule?
• Smoothing Parameter Selection in Nonparametric Regression Using an Improved Akaike Information Criterion
• Smoothers and theC_p, Generalized Maximum Likelihood, and Extended Exponential Criteria
• Some Comments on C P

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