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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Published on: July 3, 2020

Marginal longitudinal semiparametric regression via penalized splines.

M Al Kadiri1, R J Carroll, M P Wand

  • 1Centre for Statistical and Survey Methodology, School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, New South Wales, Australia.

Statistics & Probability Letters
|November 2, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a simple penalized spline method for marginal longitudinal nonparametric regression. This approach, implemented using Gibbs sampling and BUGS software, offers an efficient estimation technique.

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Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Marginal longitudinal nonparametric regression is complex.
  • Existing efficient estimation methods are often elaborate.
  • A simpler, effective approach is needed.

Purpose of the Study:

  • To present a straightforward penalized spline method for marginal longitudinal nonparametric regression.
  • To demonstrate the utility of Gibbs sampling and BUGS software for implementation.
  • To illustrate applications in nonparametric and additive regression models.

Main Methods:

  • Penalized splines for nonparametric regression.
  • Gibbs sampling for Bayesian inference.
  • BUGS software for computational implementation.

Main Results:

  • A simple and effective penalized spline approach for marginal longitudinal nonparametric regression.
  • Efficient estimation is achievable with this method.
  • Successful implementation using Gibbs sampling and BUGS.

Conclusions:

  • Penalized splines offer a practical solution for marginal longitudinal nonparametric regression.
  • Gibbs sampling and BUGS facilitate efficient implementation.
  • The method is applicable to various regression models.