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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

Bayesian information criterion for longitudinal and clustered data.

Richard H Jones1

  • 1Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver, Aurora, USA. richard.jones@UCDenver.edu

Statistics in Medicine
|August 2, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces an effective sample size calculation for mixed models, improving the Bayesian Information Criterion (BIC) for non-independent data. This method enhances model selection accuracy when dealing with complex data structures.

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Area of Science:

  • Statistics
  • Statistical Modeling

Background:

  • Model selection criteria like Akaike's Information Criterion (AIC) and Bayesian Information Criterion (BIC) are crucial for choosing the best statistical model.
  • AIC penalizes models based on the number of estimated parameters, while BIC uses a penalty related to the sample size.
  • A key assumption of BIC is independent observations, which is violated in mixed models.

Purpose of the Study:

  • To develop a method for calculating an 'effective sample size' specifically for mixed models.
  • To adapt the Bayesian Information Criterion (BIC) for use with mixed models by incorporating this effective sample size.

Main Methods:

  • The study proposes calculating the effective sample size for mixed models using Fisher's information.
  • This effective sample size is then used to modify the BIC penalty term.
  • Various error models within a general mixed model framework are considered, including unstructured and compound symmetry.

Main Results:

  • The calculated effective sample size can range from the number of subjects to the total number of observations.
  • This method provides a more appropriate penalty for model complexity in mixed models compared to standard BIC.
  • The approach allows for more nuanced model selection when observations are not independent.

Conclusions:

  • The developed method for effective sample size enhances the applicability of BIC to mixed models.
  • This provides a valuable tool for researchers using mixed models to select the most parsimonious and best-fitting model.
  • Accurate model selection is vital for reliable statistical inference.