for misspecified regression models

DOI10.2307/3315853zbMath1050.62080OpenAlexW2061052065MaRDI QIDQ4819897

Julie Zhou, Peilin Shi, Jane J. Ye

Publication date: 5 October 2004

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

Full work available at URL: https://doi.org/10.2307/3315853

zbMATH Keywords

nonsmooth optimizationClarke generalized gradientleast squaresbiased regressionminimax designrobust regression designnormalizing optimization method

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Linear regression; mixed models (62J05) Optimal statistical designs (62K05) Robustness and adaptive procedures (parametric inference) (62F35)

Related Items (12)

Some robust designs for polynomial regression models ⋮ I-robust and D-robust designs on a finite design space ⋮ Existence and symmetry of minimax regression designs ⋮ Optimal and robust designs for trigonometric regression models ⋮ Applications and Implementations of Continuous Robust Designs ⋮ D-optimal minimax regression designs on discrete design space ⋮ Robust prediction and extrapolation designs for misspecified generalized linear regression models ⋮ Minimax designs for linear regression models with bias in a reproducing kernel Hilbert space in a discrete set ⋮ Discrete minimax designs for regression models with autocorrelated MA errors ⋮ Robust prediction and extrapolation designs for nonlinear regression with imprecision ⋮ Weighted least squares estimators in possibly misspecified nonlinear regression ⋮ Techniques for the construction of robust regression designs

Cites Work

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• Robust minimax designs for multiple linear regression
• Simultaneous equations models in applied search theory
• Minimax designs for approximately linear regression
• Robust designs for approximately polynomial regression
• Restricted minimax robust designs for misspecified regression models
• Optimization and nonsmooth analysis
• Robust optimal extrapolation designs
• Designs for approximately linear regression: Maximizing the minimum coverage probability of confidence ellipsoids
• Optimal Experimental Design for Polynomial Regression
• A Basis for the Selection of a Response Surface Design

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