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Fixed and Random Effects Selection by REML and Pathwise Coordinate Optimization.

Bingqing Lin1, Zhen Pang1, Jiming Jiang2

  • 1Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371.

Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
|June 13, 2014
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Summary
This summary is machine-generated.

We developed a two-stage method for selecting effects in linear mixed-effects models. This approach efficiently identifies both fixed and random effects, demonstrating oracle properties for reliable model selection.

Keywords:
BICLASSOMixed-effects models

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Area of Science:

  • Statistics
  • Statistical Modeling

Background:

  • Linear mixed-effects models are widely used but selecting appropriate fixed and random effects can be challenging.
  • Existing methods may lack efficiency or theoretical guarantees for simultaneous effect selection.

Purpose of the Study:

  • To propose a novel two-stage model selection procedure for linear mixed-effects models.
  • To ensure the proposed procedure possesses oracle properties, guaranteeing optimal selection consistency.
  • To enhance computational efficiency in selecting both fixed and random effects.

Main Methods:

  • A two-stage procedure involving penalized restricted log-likelihood for random effects selection using a Newton-type algorithm.
  • Penalized log-likelihood with pathwise coordinate optimization for efficient fixed effects selection.
  • Theoretical proofs establishing the oracle properties of the proposed procedure.

Main Results:

  • The proposed two-stage procedure effectively selects both fixed and random effects in linear mixed-effects models.
  • The method demonstrates oracle properties, indicating asymptotically optimal selection.
  • Simulation studies and a real data example confirm the finite sample performance.

Conclusions:

  • The developed two-stage model selection procedure offers an efficient and theoretically sound approach for linear mixed-effects models.
  • The procedure's ability to select both fixed and random effects with oracle properties is a significant advancement.
  • The findings are validated through empirical studies, supporting its practical applicability.