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

Published on: July 3, 2020

Regression analysis using dependent Polya trees.

Angela Schörgendorfer1, Adam J Branscum

  • 1IBM T.J. Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, New York, 10598, U.S.A.

Statistics in Medicine
|July 11, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a flexible semiparametric Bayesian regression model. It accurately captures evolving residual distributions, outperforming existing methods in analyzing complex biological data.

Keywords:
Bayesian nonparametricsmixtures of finite Polya treessemiparametric regression

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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: January 11, 2020

Area of Science:

  • Statistics
  • Biostatistics
  • Computational Biology

Background:

  • Traditional linear regression models impose restrictive assumptions on data's residual structure.
  • There is a need for more flexible models to capture complex, evolving residual distributions in data.

Purpose of the Study:

  • To propose a novel semiparametric Bayesian regression model for analyzing data with arbitrary and evolving residual distributions.
  • To develop a flexible modeling approach for various regression settings, including repeated measures.

Main Methods:

  • Utilized a novel dependent Polya tree prior to model arbitrary residual distributions.
  • Allowed residual distributions to evolve across increasing levels of an ordinal covariate (e.g., time).
  • Applied the model to cross-sectional, prospective, and repeated measurement data, including fixed-effects and mixed-effects models.

Main Results:

  • A simulation study demonstrated the model's flexibility in accurately capturing evolving residual distributions.
  • The proposed model outperformed contemporary semiparametric models in an application to immunoglobulin G antibody data in children.

Conclusions:

  • The novel semiparametric Bayesian model offers a flexible and data-driven approach to regression analysis.
  • This method effectively models changing residual structures, showing superior performance in real-world biological data analysis.