Literature DB >> 31423293

Letter to the Editor.

Marco Geraci1.   

Abstract

Galarza, Lachos and Bandyopadhyay (2017) have recently proposed a method of estimating linear quantile mixed models (Geraci and Bottai, 2014) based on a Monte Carlo EM algorithm. They assert that their procedure represents an improvement over the numerical quadrature and non-smooth optimization approach implemented by Geraci (2014). The objective of this note is to demonstrate that this claim is incorrect. We also point out several inaccuracies and shortcomings in their paper which affect other results and conclusions that can be drawn.

Entities:  

Year:  2018        PMID: 31423293      PMCID: PMC6697264          DOI: 10.4310/SII.2019.v12.n1.a7

Source DB:  PubMed          Journal:  Stat Interface        ISSN: 1938-7989            Impact factor:   0.582


  3 in total

1.  Linear mixed models with flexible distributions of random effects for longitudinal data.

Authors:  D Zhang; M Davidian
Journal:  Biometrics       Date:  2001-09       Impact factor: 2.571

2.  Quantile regression for longitudinal data using the asymmetric Laplace distribution.

Authors:  Marco Geraci; Matteo Bottai
Journal:  Biostatistics       Date:  2006-04-24       Impact factor: 5.899

3.  Quantile regression in linear mixed models: a stochastic approximation EM approach.

Authors:  Christian E Galarza; Victor H Lachos; Dipankar Bandyopadhyay
Journal:  Stat Interface       Date:  2017       Impact factor: 0.582

  3 in total

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