A modified information criterion for model selection

DOI10.1080/03610926.2019.1708395OpenAlexW2997895612MaRDI QIDQ5079975

Emre Dünder

Publication date: 30 May 2022

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

Full work available at URL: https://doi.org/10.1080/03610926.2019.1708395

zbMATH Keywords

Lasso regressionshrinkage covariance matrixFisher information criterion

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• glmnet
• PERregress

Cites Work

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• A novel hybrid dimension reduction technique for undersized high dimensional gene expression data sets using information complexity criterion for cancer classification
• Model selection in multivariate adaptive regression splines (MARS) using information complexity as the fitness function
• Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions
• Empirical Bayes estimation of the multivariate normal covariance matrix
• On predictive least squares principles
• Estimating the dimension of a model
• Tuning parameter selection in sparse regression modeling
• Akaike's information criterion and recent developments in information complexity
• Modified versions of the Bayesian information criterion for sparse generalized linear models
• Information criteria and statistical modeling.
• Extended BIC for small-n-large-P sparse GLM
• Bias of the corrected AIC criterion for underfitted regression and time series models
• Maximum likelihood identification of Gaussian autoregressive moving average models
• Tuning Parameter Selection for the Adaptive Lasso Using ERIC