Variable selection via penalized minimum φ-divergence estimation in logistic regression

DOI10.1080/02664763.2013.864262zbMath1352.62046OpenAlexW2043511775MaRDI QIDQ3179244

D. M. Sakate, Dattatraya N. Kashid

Publication date: 21 December 2016

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

Full work available at URL: https://doi.org/10.1080/02664763.2013.864262

zbMATH Keywords

logistic regressionvariable selection\(\phi\)-divergenceSCADpenalized MLE

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Generalized linear models (logistic models) (62J12)

Related Items (1)

Robust variable selection via penalized MT-estimator in generalized linear models

Cites Work

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• Minimum \(\phi\)-divergence estimator in logistic regression models
• A new family of BAN estimators for polytomous logistic regression models based on \(\varphi\)-diver\-gence measures
• SCAD-penalized regression in high-dimensional partially linear models
• Minimum \(\varphi\)-divergence estimator and \(\varphi\)-divergence statistics in generalized linear models with binary data
• Theory of statistical inference and information. Transl. from the Slovak by the author
• Nonconcave penalized likelihood with a diverging number of parameters.
• Penalized maximum likelihood estimation in logistic regression and discrimination
• Ideal spatial adaptation by wavelet shrinkage
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Minimum power-divergence estimator in three-way contingency tables
• Smoothly Clipped Absolute Deviation on High Dimensions
• Tuning parameter selectors for the smoothly clipped absolute deviation method

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