Year: 2016
Type: book-chapter
Abstract: We propose a new method to select the tuning parameter in lasso regression. Unlike the previous proposals, the method is iterative and thus it is particularly efficient when multiple tuning parameters have to be selected. The method also applies to more general regression frameworks, such as generalized linear models with non-normal responses. Simulation studies show our proposal performs well, and most of times, better when compared with the traditional Bayesian Information Criterion and Cross validation.
Authors: Gianluca Sottile, Vmr Muggeo
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Funders: Fundação de Amparo à Pesquisa do Estado de São Paulo, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Austrian Science Fund, Eusko Jaurlaritza, Ministerio de Educación, Cultura y Deporte, Ministerio de Economía y Competitividad, Euskal Herriko Unibertsitatea, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação Araucária, Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco, African Union, Basque Center for Applied Mathematics, Engineering and Physical Sciences Research Council, Fundação para a Ciência e a Tecnologia
OpenAlex ID: W2766375538