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Penalized likelihood and Bayesian function selection in regression models

Content Provider	Springer Nature Link
Author	Scheipl, Fabian Kneib, Thomas Fahrmeir, Ludwig
Copyright Year	2013
Abstract	Challenging research in various fields has driven a wide range of methodological advances in variable selection for regression models with high-dimensional predictors. In comparison, selection of nonlinear functions in models with additive predictors has been considered only more recently. Several competing suggestions have been developed at about the same time and often do not refer to each other. This article provides a state-of-the-art review on function selection, focusing on penalized likelihood and Bayesian concepts, relating various approaches to each other in a unified framework. In an empirical comparison, also including boosting, we evaluate several methods through applications to simulated and real data, thereby providing some guidance on their performance in practice.
Starting Page	349
Ending Page	385
Page Count	37
File Format	PDF
ISSN	18638171
Journal	AStA Advances in Statistical Analysis
Volume Number	97
Issue Number	4
e-ISSN	1863818X
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-04-07
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Generalized additive model Regularization Smoothing Spike and slab priors Statistics Statistics for Business/Economics/Mathematical Finance/Insurance Probability Theory and Stochastic Processes Econometrics
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Statistics and Probability Social Sciences Economics and Econometrics Analysis Modeling and Simulation

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