Variational approximations in geoadditive latent Gaussian regression: mean and quantile regression View Full Text


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Article Info

DATE

2015-11

AUTHORS

Elisabeth Waldmann, Thomas Kneib

ABSTRACT

Geoadditive regression models define a comprehensive class of statistical models that allow to capture a variety of different effects on a response variable of interest, including nonlinear effects of continuous covariates and spatial effects as special cases. We develop variational approximations for Bayesian inference in geoadditive regression models to provide a computationally attractive, fast alternative to Markov chain Monte Carlo simulations. Therefore we consider the class of latent Gaussian regression models where the distribution of the response can be represented as a location-scale mixture of Gaussians such that the calculation of quasi-full conditionals in the variational approximations is considerably facilitated. As special cases, we consider mean and quantile regression and evaluate the novel variational Bayes approaches in a simulation study. As an application, we focus on the analysis of technical efficiencies of British and Welsh farms where the response variable of interest is the output produced by a farm given specific input covariates. More... »

PAGES

1247-1263

References to SciGraph publications

  • 2014-03. Multilevel structured additive regression in STATISTICS AND COMPUTING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11222-014-9480-2

    DOI

    http://dx.doi.org/10.1007/s11222-014-9480-2

    DIMENSIONS

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