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Estimation and selection in linear mixed models with missing data under compound symmetric structure.

Yi-Ching Lee1, Junfeng Shang2

  • 1Department of Mathematics, Southeast Missouri State University, Cape Girardeau, MO, USA.

Journal of Applied Statistics
|November 3, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for handling missing data in linear mixed-effects models by using an indicator-based matrix. The approach effectively estimates parameters and aids in model selection, even with high missing rates.

Keywords:
Model selectioncompound symmetric structurecoverage probabilityestimation accuracy and precisionlinear mixed modelsmissing rate

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Area of Science:

  • Statistics
  • Biostatistics
  • Data Science

Background:

  • Classical linear models struggle with correlated responses and missing data.
  • Linear mixed-effects models (LMMs) address data dependency using random effects.
  • Missing values are common in real-world data due to dropouts or non-responses.

Purpose of the Study:

  • To investigate estimation and model selection performance in LMMs with missing data.
  • To develop a robust method for parameter estimation in the presence of missingness.
  • To analyze the impact of missing rates on estimation and model selection.

Main Methods:

  • Proposed an indicator-based matrix approach to record missingness.
  • Derived likelihood-based estimators for LMM parameters.
  • Utilized simulations and a real data application for validation.

Main Results:

  • The proposed method effectively estimates parameters in LMMs with missing data.
  • Demonstrated the relationship between estimation and model selection across varying missing rates.
  • The approach proved effective in selecting appropriate models.

Conclusions:

  • The indicator-based matrix method offers a robust solution for handling missing data in LMMs.
  • This technique enhances both parameter estimation and model selection accuracy.
  • Effective for real-world applications with incomplete datasets.