Journal Article

Variable Selection and Model Choice in Geoadditive Regression Models

Kneib, Thomas, Hothorn, Torsten, Tutz, Gerhard

2009

Overview

Model choice and variable selection are issues of major concern in practical regression analyses, arising in many biometric applications such as habitat suitability analyses, where the aim is to identify the influence of potentially many environmental conditions on certain species. We describe regression models for breeding bird communities that facilitate both model choice and variable selection, by a boosting algorithm that works within a class of geoadditive regression models comprising spatial effects, nonparametric effects of continuous covariates, interaction surfaces, and varying coefficients. The major modeling components are penalized splines and their bivariate tensor product extensions. All smooth model terms are represented as the sum of a parametric component and a smooth component with one degree of freedom to obtain a fair comparison between the model terms. A generic representation of the geoadditive model allows us to devise a general boosting algorithm that automatically performs model choice and variable selection.

• Publisher: Blackwell Publishing Inc,Wiley-Blackwell Publishing,Blackwell Publishing Ltd
• Subject: Algorithms / Animal populations / Animals / Biometric Practice / Biometrics / biometry / Biometry - methods / Biostatistics / birds / Birds - physiology / Bivariate smoothing / Boosting / Breeding / Computer Simulation / Data Interpretation, Statistical / Degrees of freedom / Ecosystem / environmental factors / Functional gradient / Habitat selection / Habitats / Models, Biological / Models, Statistical / Nonparametric models / Parametric models / Penalized splines / Polynomials / Population Dynamics / Regression analysis / Regression coefficients / Space-varying effects / Spatial models / Statistical methods / Tensors