A regression model selection criterion based on bootstrap bumping for use with resistant fitting.

DOI10.1016/S0167-9473(00)00010-4zbMath1110.62313OpenAlexW2038395652MaRDI QIDQ5940722

Joseph E. Cavanaugh, Andrew A. Neath

Publication date: 20 August 2001

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0167-9473(00)00010-4

zbMATH Keywords

Akaike information criterionRegressionBootstrap bumpingKullback--Leibler informationLeast median of squaresModel selection criterion

Mathematics Subject Classification ID

Related Items (3)

APPLIED REGRESSION ANALYSIS BIBLIOGRAPHY UPDATE 2000–2001 ⋮ A jackknife type approach to statistical model selection ⋮ Statistical inference for a general class of distributions with time-varying parameters

Cites Work

• The feasible set algorithm for least median of squares regression
• Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation
• Least Median of Squares Regression
• How Biased is the Apparent Error Rate of a Prediction Rule?
• Regression and time series model selection in small samples
• A new look at the statistical model identification
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