- Title
- Marginal longitudinal curves estimated via Bayesian penalized splines
- Creator
- Al Kadiri, Mohammad; Bani-Mustafa, Ahmed; Finch, Caroline
- Date
- 2010
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/103169
- Identifier
- vital:10846
- Abstract
- The six cities air pollution is used to estimate and investigate the marginal curve of a function describing lung growth for set of children in a longitudinal study. This article proposes penalized regression spline technqiue based ona semiparametric mixed models (MM) framework for an additive model. This smoothing approach fits marginal models for longitudinal unbalanced measurements by using a Bayesian inference approach, implemented using a Markov chain Monte Carlo approach with the Gibbs sampler. The unbalanced case in which missing or different number of measurements for a set of subjects is more practical and common in real life studies. This methodology makes it possible to establish a straightforward approach to similar models using R programming, when it is not possible to do so using existing codes.
- Publisher
- Elsevier
- Relation
- Australian Statistical Conference 2010 , 6th December, 2010 Fremantle Published in Statistics & Probability Letters Vol. 80 Issue 15-16 Vol. 80, p. 1-19
- Rights
- Copyright Elsevier
- Rights
- This metadata is freely available under a CCO license
- Subject
- Best prediction; Markov chain Monte Carlo; Semiparametric regression'; Restricted maximum likelihood; Additive models; Hyperparameters
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