Variable selection for sparse high-dimensional nonlinear regression models by combining nonnegative garrote and sure independence screening

DOI10.5705/ss.2012.316OpenAlexW2313341823MaRDI QIDQ5248920

Hulin Wu, Yichao Wu, Shuang Wu, Hongqi Xue

Publication date: 28 April 2015

Published in: Statistica Sinica (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc4142445

zbMATH Keywords

sigmoid functionsure screeningnonnegative garrotegene regulationsnonlinear regressionsindependence learning

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items

A novel bagging approach for variable ranking and selection via a mixed importance measure ⋮ Conditional SIRS for nonparametric and semiparametric models by marginal empirical likelihood ⋮ Variable screening for high dimensional time series ⋮ Additive model selection ⋮ Bayesian inference of a directional brain network model for intracranial EEG data ⋮ Approximate large-scale Bayesian spatial modeling with application to quantitative magnetic resonance imaging