A new type of generalized information criterion for regularization parameter selection in penalized regression with application to treatment process data

We propose a new approach to select the regularization parameter using a new version of the generalized information criterion ( $GIC$ GIC ) in the subject of penalized regression. We prove the identifiability of bridge regression model as a prerequisite of statistical modeling. Then, we propose asym...

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Bibliographic Details
Published inJournal of biopharmaceutical statistics Vol. 34; no. 4; pp. 488 - 512
Main Authors Ghatari, Amir Hossein, Aminghafari, Mina
Format Journal Article
LanguageEnglish
Published England Taylor & Francis 03.07.2024
Subjects
Asymptotic Loss Efficiency
Breast Neoplasms - drug therapy
Computer Simulation
Data Interpretation, Statistical
Female
generalized information criterion
Humans
Male
Models, Statistical
Parkinson Disease - diagnosis
Parkinson Disease - drug therapy
penalized regression
Prostatic Neoplasms - drug therapy
Regression Analysis
regularization parameter
utility function
utility function
generalized information criterion
regularization parameter
penalized regression
Asymptotic Loss Efficiency
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Summary:We propose a new approach to select the regularization parameter using a new version of the generalized information criterion ( $GIC$ GIC ) in the subject of penalized regression. We prove the identifiability of bridge regression model as a prerequisite of statistical modeling. Then, we propose asymptotically efficient generalized information criterion ( $AGIC$ AGIC ) and prove that it has asymptotic loss efficiency. Also, we verified the better performance of $AGIC$ AGIC in comparison to the older versions of $GIC$ GIC . Furthermore, we propose $MSE$ MSE search paths to order the selected features by lasso regression based on numerical studies. The $MSE$ MSE search paths provide a way to cover the lack of feature ordering in lasso regression model. The performance of $AGIC$ AGIC with other types of $GIC$ GIC is compared using $MSE$ MSE and model utility in simulation study. We exert $AGIC$ AGIC and other criteria to analyze breast and prostate cancer and Parkinson disease datasets. The results confirm the superiority of $AGIC$ AGIC in almost all situations.
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ISSN:1054-3406
1520-5711
1520-5711
DOI:10.1080/10543406.2023.2228399