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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

Quasi-least squares with mixed linear correlation structures.

Jichun Xie1, Justine Shults, Jon Peet

  • 1Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Tel: (215) 573-8950.

Statistics and Its Interface
|April 21, 2012
PubMed
Summary
This summary is machine-generated.

Quasi-least squares (QLS) provides a robust method for estimating correlation parameters in generalized estimating equations. This study proves QLS estimators are reliable for mixed linear correlation structures, crucial for family health data analysis.

Related Experiment Videos

Last Updated: May 23, 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
  • Biostatistics
  • Genetics

Background:

  • Generalized estimating equations (GEE) are widely used for correlated data.
  • Accurate estimation of correlation structures is vital for valid statistical inference.
  • Mixed linear correlation structures are suitable for analyzing familial data with varying sizes.

Purpose of the Study:

  • To establish theoretical guarantees for Quasi-least squares (QLS) estimators in mixed linear correlation models.
  • To demonstrate the practical application and benefits of QLS for familial data analysis.
  • To provide accessible R software for medical researchers.

Main Methods:

  • Developed and proved theoretical properties of two-stage QLS estimators.
  • Applied QLS to analyze optical spherical values in the Old Order Amish (OOA) cohort.
  • Utilized familial correlation structures to model genetic and environmental influences.

Main Results:

  • QLS stage one estimators always exist and are feasible for any correlation structure.
  • QLS stage two estimators exist and are unique with probability one for mixed linear correlation structures.
  • Misspecification of familial structures leads to significant efficiency loss in OOA data analysis.

Conclusions:

  • QLS is a theoretically sound and practically useful method for analyzing correlated familial data.
  • Accurate specification of correlation structures, particularly familial ones, is essential for efficient statistical analysis.
  • The provided R software facilitates the implementation of QLS for medical research.