Concave group methods for variable selection and estimation in high-dimensional varying coefficient models

Guangren Yang; Jian Huang; Yong Zhou

doi:10.1007/s11425-014-4842-y

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Concave group methods for variable selection and estimation in high-dimensional varying coefficient models

Guangren Yang Jian Huang Yong Zhou

Year: 2014 Journal: Science China Mathematics Vol: 57 (10)Pages: 2073-2090 Publisher: Springer Nature

DOI: 10.1007/s11425-014-4842-y

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Keywords:

Lasso (programming language) Mathematics Minimax Feature selection Oracle Selection (genetic algorithm) Estimator Model selection Group (periodic table) Mathematical optimization Function (biology) Applied mathematics Statistics Computer science Machine learning

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Statistical Methods and Inference

Physical Sciences → Mathematics → Statistics and Probability

Bayesian Methods and Mixture Models

Physical Sciences → Computer Science → Artificial Intelligence

Statistical Methods and Bayesian Inference

Physical Sciences → Mathematics → Statistics and Probability

Concave group methods for variable selection and estimation in high-dimensional varying coefficient models

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