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This summary is machine-generated.

This study introduces a doubly regularized linear mixed-effects model (LMM) for high-dimensional longitudinal data. The method effectively selects fixed and random effects, offering consistent estimation and variable selection.

Keywords:
Diverging ratePrimary 62J05, 62J07Random-effects selectionRegularizationVariable selectionsecondary 62F12

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Area of Science:

  • Statistics
  • Biostatistics
  • Data Science

Background:

  • Linear mixed-effects models (LMMs) are standard for clustered/longitudinal data.
  • High-dimensional longitudinal data present estimation and selection challenges.
  • Existing methods may struggle with the complexity of high-dimensional LMMs.

Purpose of the Study:

  • To develop a robust method for analyzing high-dimensional longitudinal data using LMMs.
  • To simultaneously select and estimate fixed and random effects in LMMs.
  • To address theoretical and computational challenges in high-dimensional LMM applications.

Main Methods:

  • A doubly regularized approach is proposed for LMMs.
  • The method simultaneously selects fixed and random effects.
  • New regularity conditions are established for theoretical guarantees.

Main Results:

  • The proposed method achieves estimation and selection consistency in high-dimensional settings.
  • Theoretical properties are established for large sample sizes where effects exceed sample size.
  • A novel algorithm offers efficient computation comparable to standard methods.

Conclusions:

  • The doubly regularized LMM effectively handles high-dimensional longitudinal data.
  • The method provides reliable variable selection and parameter estimation.
  • It offers a computationally efficient solution for complex data structures.