Linear regression with bivariate response variable containing missing data. Strategies to increase prediction precision

DOI10.1080/03610918.2019.1656249OpenAlexW2971543832MaRDI QIDQ5082834

Trygve Almøy, Lars Erik Gangsei, Solve Saebo

Publication date: 21 June 2022

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2019.1656249

zbMATH Keywords

prediction errormissing databivariate linear regression

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07)

Uses Software

• R
• Systemfit

Cites Work

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• Families of minimax estimators of the mean of a multivariate normal distribution
• A Dictionary of Statistics
• Model Selection and Model Averaging
• Data Analysis Using Stein's Estimator and its Generalizations
• The Focused Information Criterion
• Theoretical evaluation of prediction error in linear regression with a bivariate response variable containing missing data
• Bayes Factors
• Stein's Estimation Rule and Its Competitors--An Empirical Bayes Approach
• Limiting the Risk of Bayes and Empirical Bayes Estimators--Part I: The Bayes Case
• Limiting the Risk of Bayes and Empirical Bayes Estimators--Part II: The Empirical Bayes Case
• Empirical Bayes on vector observations: An extension of Stein's method

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