Minimax Linear and Quadratic Estimators in Semiparametric Multivariate Regression Models

DOI10.1080/02331889908802679zbMath0955.62056OpenAlexW2033559585MaRDI QIDQ4943298

Olaf Bunke

Publication date: 27 February 2001

Published in: Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331889908802679

zbMATH Keywords

semiparametric modelsvariance estimationcovariance estimationbest quadratic estimators

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05)

Cites Work

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• Bootstrap and cross-validation estimates of the prediction error for linear regression models
• Spline smoothing and optimal rates of convergence in nonparametric regression models
• Estimation of heteroscedasticity in regression analysis
• Asymptotically optimal difference-based estimation of variance in nonparametric regression
• Statistische Methoden der Modellbildung, III
• Residual variance and residual pattern in nonlinear regression
• Bayes, Admissible, and Minimax Linear Estimators in Linear Models with Restricted Parameter Space
• Second Order Asymptotic Risks of Smoothed Linear Estimators in Nonparametric Regression Models

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