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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 for nonlinear mixed effects models: a likelihood based perspective

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Author(s):
Galarza, Christian E. [1, 2] ; Castro, Luis M. [3] ; Louzada, Francisco [4] ; Lachos, Victor H. [5]
Total Authors: 4
Affiliation:
[1] Univ Estadual Campinas, Dept Stat, Sao Paulo - Brazil
[2] Escuela Super Politecn Litoral, ESPOL, Dept Matemat, Guayaquil - Ecuador
[3] Pontificia Univ Catolica Chile, Dept Stat, Casilla 306, Correo 22, Santiago - Chile
[4] Univ Sao Paulo, Dept Appl Math & Stat, Sao Carlos - Brazil
[5] Univ Connecticut, Dept Stat, Storrs, CT 06269 - USA
Total Affiliations: 5
Document type: Journal article
Source: STATISTICAL PAPERS; v. 61, n. 3, p. 1281-1307, JUN 2020.
Web of Science Citations: 0
Abstract

Longitudinal data are frequently analyzed using normal mixed effects models. Moreover, the traditional estimation methods are based on mean regression, which leads to non-robust parameter estimation under non-normal error distribution. However, at least in principle, quantile regression (QR) is more robust in the presence of outliers/influential observations and misspecification of the error distributions when compared to the conventional mean regression approach. In this context, this paper develops a likelihood-based approach for estimating QR models with correlated continuous longitudinal data using the asymmetric Laplace distribution. Our approach relies on the stochastic approximation of the EM algorithm (SAEM algorithm), obtaining maximum likelihood estimates of the fixed effects and variance components in the case of nonlinear mixed effects (NLME) models. We evaluate the finite sample performance of the SAEM algorithm and asymptotic properties of the ML estimates through simulation experiments. Moreover, two real life datasets are used to illustrate our proposed method using the qrNLMM package from R. (AU)

FAPESP's process: 15/17110-9 - Robust Estimation in Spatial Models for Censored Data
Grantee:Christian Eduardo Galarza Morales
Support Opportunities: Scholarships in Brazil - Doctorate
FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:Francisco Louzada Neto
Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC