A minimum matrix valued risk estimator combining restricted and ordinary least squares estimators

DOI10.1080/03610926.2021.1934032OpenAlexW3171152444MaRDI QIDQ6107591

Nimet Özbay, Buatikan Mirezi, Selahattin Kaçıranlar

Publication date: 3 July 2023

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2021.1934032

zbMATH Keywords

convex optimizationmean square errorordinary least squaresrestricted least squares

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05) Applications of functional analysis in optimization, convex analysis, mathematical programming, economics (46N10) Applications of operator theory in optimization, convex analysis, mathematical programming, economics (47N10)

Cites Work

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• Linear models. Least squares and alternatives
• Mean square error matrix comparisons of estimators in linear regression
• The Minimum Mean Square Error Linear Estimator and Ridge Regression
• A Monte Carlo Evaluation of Some Ridge-Type Estimators
• Performance of Some New Ridge Regression Estimators
• Linear Statistical Inference and its Applications
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