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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Related Experiment Video

Updated: Sep 8, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Unit-Lindley mixed-effect model for proportion data.

Hatice Tul Kubra Akdur1

  • 1Department of Statistics, Faculty of Science, Gazi University, Ankara, Turkey.

Journal of Applied Statistics
|June 16, 2022
PubMed
Summary
This summary is machine-generated.

The new unit-Lindley mixed-effect model accurately analyzes proportion data, outperforming existing models. This statistical approach is valuable for hierarchical data in fields like public health and economics.

Keywords:
Proportion datahouseholds with poor qualitylikelihood approximationmixed-effect modelsunit-Lindley distribution

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Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Proportion data with hierarchical structures are common in clinical trials, economics, and social studies.
  • The unit-Lindley distribution offers an alternative to the Beta regression model for continuous outcomes within the unit interval.
  • Mixed-effect models are essential for analyzing clustered or longitudinal data.

Purpose of the Study:

  • To propose a unit-Lindley mixed-effect model for analyzing hierarchical proportion data.
  • To investigate likelihood analysis methods for parameter estimation in this model.
  • To evaluate the model's performance against existing methods and assess approximation techniques.

Main Methods:

  • Development of the unit-Lindley mixed-effect model.
  • Application of Laplace and adaptive Gaussian quadrature approximation methods for parameter estimation.
  • Analysis of a Brazilian dataset on household water supply and sewage using the proposed model with random intercepts for federative states.
  • Monte Carlo simulation study to evaluate the accuracy (bias and mean square error) of estimation methods.

Main Results:

  • The unit-Lindley mixed-effect model demonstrated a superior fit compared to the standard unit-Lindley regression model and the Beta mixed model.
  • Laplace and adaptive Gaussian quadrature methods provided accurate parameter estimates in the simulation study.
  • The model effectively analyzed the Brazilian dataset, incorporating hierarchical structures.

Conclusions:

  • The proposed unit-Lindley mixed-effect model is a robust and effective tool for analyzing hierarchical proportion data.
  • The employed approximation methods are reliable for parameter estimation in complex mixed-effect models.
  • This statistical framework offers significant advantages for research in public health, economics, and social sciences dealing with proportion data.