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Conditional variable importance for random forests
A new, conditional permutation scheme is developed for the computation of the variable importance measure that reflects the true impact of each predictor variable more reliably than the original marginal approach. Expand
Regression: Models, Methods and Applications
The Classical Linear Model is extended to include nonparametric Regression and Structured Additive Regression in the model of Quantile Regression. Expand
We propose extensions of penalized spline generalized additive models for analyzing space-time regression data and study them from a Bayesian per- spective. Non-linear effects of continuousExpand
On the behaviour of marginal and conditional AIC in linear mixed models
In linear mixed models, model selection frequently includes the selection of random effects. Two versions of the Akaike information criterion, aic , have been used, based either on the marginal or onExpand
Identifying Risk Factors for Severe Childhood Malnutrition by Boosting Additive Quantile Regression
We investigated the risk factors for childhood malnutrition in India based on the 2005/2006 Demographic and Health Survey by applying a novel estimation technique for additive quantile regression.Expand
BayesX: Analyzing Bayesian Structural Additive Regression Models
There has been much recent interest in Bayesian inference for generalized additive and related models. The increasing popularity of Bayesian methods for these and other model classes is mainly causedExpand
Mixed model based inference in structured additive regression
  • T. Kneib
  • Computer Science
  • 22 February 2006
Several possibilities to model non-standard covariate effects such as nonlinear effects of continuous covariates, temporal effects, spatial effects, interaction effects or unobserved heterogeneity are reviewed and embedded in the general framework of structured additive regression. Expand
Model-based Boosting 2.0
We describe version 2.0 of the R add-on package mboost. The package implements boosting for optimizing general risk functions using component-wise (penalized) least squares estimates or regressionExpand
A Mixed Model Approach for Geoadditive Hazard Regression
Mixed model based approaches for semiparametric regression have gained much interest in recent years, both in theory and application. They provide a unified and modular framework for penalizedExpand
Spike-and-Slab Priors for Function Selection in Structured Additive Regression Models
Structured additive regression (STAR) provides a general framework for complex Gaussian and non-Gaussian regression models, with predictors comprising arbitrary combinations of nonlinear functionsExpand