Selection of the splined variables and convergence rates in a partial spline model

DOI10.2307/3315397zbMath0737.62033OpenAlexW2140807184MaRDI QIDQ3985983

Keh-Wei Chen, Hung Chen

Publication date: 27 June 1992

Published in: Canadian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.2307/3315397

zbMATH Keywords

rate of convergenceoptimal rate of convergenceunbiased risk estimationdata-driven methodprobability of correct selectionall-subset selectionexploratory partial spline modelgeneral class of smooth regression functionsprinciple of unbiased risk estimation

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Linear inference, regression (62J99)

Related Items (7)

Consistent covariate selection and post model selection inference in semiparametric regression. ⋮ Semiparametric approximation methods in multivariate model selection ⋮ Mixing partially linear regression models ⋮ Robust estimators in semiparametric partly linear regression models. ⋮ Two-stage model selection procedures in partially linear regression ⋮ Plug-in marginal estimation under a general regression model with missing responses and covariates ⋮ Adaptive parametric test in a semiparametric regression model

Cites Work

• Asymptotic integrated mean square error using least squares and bias minimizing splines
• Additive regression and other nonparametric models
• Convergence rates for parametric components in a partly linear model
• Optimal rates of convergence for nonparametric estimators
• Consistent nonparametric regression. Discussion
• Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation
• An optimal selection of regression variables
• Probability Inequalities for Sums of Bounded Random Variables
• Asymptotic Properties of Non-Linear Least Squares Estimators
• A new look at the statistical model identification

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