Multilevel structured additive regression View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-03

AUTHORS

Stefan Lang, Nikolaus Umlauf, Peter Wechselberger, Kenneth Harttgen, Thomas Kneib

ABSTRACT

Models with structured additive predictor provide a very broad and rich framework for complex regression modeling. They can deal simultaneously with nonlinear covariate effects and time trends, unit- or cluster-specific heterogeneity, spatial heterogeneity and complex interactions between covariates of different type. In this paper, we propose a hierarchical or multilevel version of regression models with structured additive predictor where the regression coefficients of a particular nonlinear term may obey another regression model with structured additive predictor. In that sense, the model is composed of a hierarchy of complex structured additive regression models. The proposed model may be regarded as an extended version of a multilevel model with nonlinear covariate terms in every level of the hierarchy. The model framework is also the basis for generalized random slope modeling based on multiplicative random effects. Inference is fully Bayesian and based on Markov chain Monte Carlo simulation techniques. We provide an in depth description of several highly efficient sampling schemes that allow to estimate complex models with several hierarchy levels and a large number of observations within a couple of minutes (often even seconds). We demonstrate the practicability of the approach in a complex application on childhood undernutrition with large sample size and three hierarchy levels. More... »

PAGES

223-238

References to SciGraph publications

  • 2012-06. Priors for Bayesian adaptive spline smoothing in ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS
  • 2009-12. Improved auxiliary mixture sampling for hierarchical models of non-Gaussian data in STATISTICS AND COMPUTING
  • 2009-12-29. Data Augmentation and MCMC for Binary and Multinomial Logit Models in STATISTICAL MODELLING AND REGRESSION STRUCTURES
  • 2012-01. Additive mixed models with Dirichlet process mixture and P-spline priors in ASTA ADVANCES IN STATISTICAL ANALYSIS
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