Asymptotic optimality and efficient computation of the leave-subject-out cross-validation

DOI10.1214/12-AOS1063zbMath1296.62096arXiv1302.4607MaRDI QIDQ741814

Jianhua Z. Huang, Ganggang Xu

Publication date: 15 September 2014

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1302.4607

zbMATH Keywords

cross-validationgeneralized estimating equationspenalized splinesmultiple smoothing parametersworking correlation matrices

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Estimation in multivariate analysis (62H12) Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05) Spline approximation (41A15)

Related Items

Average estimation of semiparametric models for high-dimensional longitudinal data, Partial linear model averaging prediction for longitudinal data, Multifold Cross-Validation Model Averaging for Generalized Additive Partial Linear Models, The optimal selection for restricted linear models with average estimator, Model averaging based on leave-subject-out cross-validation for vector autoregressions, Fast covariance estimation for sparse functional data, Model averaging based on leave-subject-out cross-validation, Asymptotic optimality and efficient computation of the leave-subject-out cross-validation, Distributed Generalized Cross-Validation for Divide-and-Conquer Kernel Ridge Regression and Its Asymptotic Optimality

Uses Software

• gss
• gamair

Cites Work

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