Grouped penalization estimation of the osteoporosis data in the traditional Chinese medicine

DOI10.1080/02664763.2012.724660OpenAlexW1966936884MaRDI QIDQ5128952

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Publication date: 26 October 2020

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

Full work available at URL: https://doi.org/10.1080/02664763.2012.724660

zbMATH Keywords

variable selectiongroup Lassoosteoporosistraditional Chinese medicinecategorical covariates

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

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• Asymptotics for Lasso-type estimators.
• Simultaneous analysis of Lasso and Dantzig selector
• High-dimensional graphs and variable selection with the Lasso
• p-Values for High-Dimensional Regression
• The Group Lasso for Logistic Regression
• A group bridge approach for variable selection
• Regression Shrinkage and Selection via The Lasso: A Retrospective
• A Statistical View of Some Chemometrics Regression Tools
• Model Selection and Estimation in Regression with Grouped Variables
• Tuning parameter selectors for the smoothly clipped absolute deviation method
• Convergence of a block coordinate descent method for nondifferentiable minimization

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