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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):
Galarza, Christian E. ; Lachos, Victor H. ; Bandyopadhyay, Dipankar
Total Authors: 3
Document type: Journal article
Source: STATISTICS AND ITS INTERFACE; v. 10, n. 3, p. 471-482, 2017.
Web of Science Citations: 5
Abstract

This paper develops a likelihood-based approach to analyze quantile regression (QR) models for continuous longitudinal data via the asymmetric Laplace distribution (ALD). Compared to the conventional mean regression approach, QR can characterize the entire conditional distribution of the outcome variable and is more robust to the presence of outliers and misspecification of the error distribution. Exploiting the nice hierarchical representation of the ALD, our classical approach follows a Stochastic Approximation of the EM (SAEM) algorithm in deriving exact maximum likelihood estimates of the fixed-effects and variance components. We evaluate the finite sample performance of the algorithm and the asymptotic properties of the ML estimates through empirical experiments and applications to two real life datasets. Our empirical results clearly indicate that the SAEM estimates outperforms the estimates obtained via the combination of Gaussian quadrature and non-smooth optimization routines of the Geraci and Bottai (2014) approach in terms of standard errors and mean square error. The proposed SAEM algorithm is implemented in the R. package qrLMM( ). (AU)

FAPESP's process: 15/17110-9 - Robust Estimation in Spatial Models for Censored Data
Grantee:Christian Eduardo Galarza Morales
Support type: Scholarships in Brazil - Doctorate