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Updated: May 30, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Dynamic logistic regression and dynamic model averaging for binary classification.

Tyler H McCormick1, Adrian E Raftery, David Madigan

  • 1Department of Statistics, Columbia University, New York, New York 10025, USA. tylermc@u.washington.edu

Biometrics
|August 16, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces an online classification method for dynamic environments, handling model uncertainty and changing parameters. The approach improves data analysis for time-varying data, like appendicitis treatment trends.

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Last Updated: May 30, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

  • Statistics
  • Machine Learning
  • Biostatistics

Background:

  • Model uncertainty and parameter drift are common challenges in real-world data analysis.
  • Existing methods often struggle to adapt to evolving data-generating mechanisms over time.
  • Dynamic environments require adaptive statistical procedures for accurate classification.

Purpose of the Study:

  • To develop an online binary classification procedure that addresses model uncertainty and time-varying parameters.
  • To introduce dynamic model averaging (DMA) as a method to handle changing posterior model probabilities.
  • To create an adaptive algorithm for adjusting a forgetting factor in real-time.

Main Methods:

  • Implemented dynamic model averaging (DMA) with a state-space model for parameters.
  • Utilized a Markov chain to model changes in the data-generating model over time.
  • Developed an online algorithm to adjust a forgetting factor using the posterior predictive distribution.

Main Results:

  • The proposed online procedure effectively handles model uncertainty and parameter changes.
  • Demonstrated adaptability to varying levels of change in the data-generating mechanism.
  • Successfully applied to appendicitis treatment data, capturing substantial shifts in influencing factors over time.

Conclusions:

  • The online classification method provides a robust solution for dynamic and uncertain environments.
  • Dynamic model averaging offers a flexible framework for time-varying posterior model probabilities.
  • The method enhances data analysis in fields like healthcare, with improved data privacy due to online implementation.