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Letter to the Editor.

Marco Geraci1

  • 1Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC 29208, USA.

Statistics and Its Interface
|August 20, 2019
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Summary
This summary is machine-generated.

This study refutes claims that a new Monte Carlo EM algorithm improves linear quantile mixed model estimation. The authors identify inaccuracies in the proposed method, challenging its superiority over existing approaches.

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Area of Science:

  • Statistics
  • Statistical Modeling

Background:

  • Linear quantile mixed models (LQMM) are used for analyzing complex data structures.
  • Existing estimation methods include numerical quadrature and non-smooth optimization.

Purpose of the Study:

  • To evaluate the claims made by Galarza, Lachos, and Bandyopadhyay (2017) regarding their Monte Carlo EM algorithm for LQMM.
  • To demonstrate the incorrectness of the asserted improvements over existing methods.

Main Methods:

  • Critique of the Monte Carlo EM algorithm proposed by Galarza et al. (2017).
  • Identification of specific inaccuracies and shortcomings in the cited paper.

Main Results:

  • The Monte Carlo EM algorithm proposed by Galarza et al. (2017) does not represent an improvement for estimating linear quantile mixed models.
  • Several inaccuracies and shortcomings were found in the Galarza et al. (2017) paper.

Conclusions:

  • The claims of superiority for the Monte Carlo EM algorithm in estimating LQMM are unsubstantiated.
  • The identified issues impact the validity of other results and conclusions presented by Galarza et al. (2017).