Model selection using information criteria under a new estimation method: least squares ratio

DOI10.1080/02664763.2010.545111OpenAlexW1991420691MaRDI QIDQ5124890

Eylem Deniz, J. Andrew Howe, Oguz Akbilgic

Publication date: 30 September 2020

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

Full work available at URL: https://doi.org/10.1080/02664763.2010.545111

zbMATH Keywords

model selectionsubset selectioninformation criterialeast squares ratio

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (4)

Particle swarm optimization-based variable selection in Poisson regression analysis via information complexity-type criteria ⋮ An effective approach towards efficient estimation of general linear model in case of heteroscedastic errors ⋮ An adaptive weighted least squares ratio approach for estimation of heteroscedastic linear regression model in the presence of outliers ⋮ Classification trees aided mixed regression model

Cites Work

• Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions
• Optimal subset selection. Multiple regression, interdependence and optimal network algorithms
• Estimating the dimension of a model
• Informational complexity criteria for regression models.
• Akaike's information criterion and recent developments in information complexity
• An implicit enumeration algorithm for mining high dimensional data
• Developments in Linear Regression Methodology: 1959-1982
• A Biometrics Invited Paper. The Analysis and Selection of Variables in Linear Regression
• A multivariate generalization of the power exponential family of distributions
• A Novel Regression Approach: Least Squares Ratio
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