Additive mixed models with Dirichlet process mixture and P-spline priors View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2012-01

AUTHORS

Felix Heinzl, Ludwig Fahrmeir, Thomas Kneib

ABSTRACT

Longitudinal data often require a combination of flexible time trends and individual-specific random effects. For example, our methodological developments are motivated by a study on longitudinal body mass index profiles of children collected with the aim to gain a better understanding of factors driving childhood obesity. The high amount of nonlinearity and heterogeneity in these data and the complexity of the data set with a large number of observations, long longitudinal profiles and clusters of observations with specific deviations from the population model make the application challenging and prevent the application of standard growth curve models. We propose a fully Bayesian approach based on Markov chain Monte Carlo simulation techniques that allows for the semiparametric specification of both the trend function and the random effects distribution. Bayesian penalized splines are considered for the former, while a Dirichlet process mixture (DPM) specification allows for an adaptive amount of deviations from normality for the latter. The advantages of such DPM prior structures for random effects are investigated in terms of a simulation study to improve the understanding of the model specification before analyzing the childhood obesity data. More... »

PAGES

47-68

References to SciGraph publications

  • 2004-10. Breast-feeding and childhood obesity—a systematic review in INTERNATIONAL JOURNAL OF OBESITY
  • 2001-03. Bayesian Semiparametric Regression Analysis of Multicategorical Time-Space Data in ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10182-011-0161-6

    DOI

    http://dx.doi.org/10.1007/s10182-011-0161-6

    DIMENSIONS

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