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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.)

Robust quantile regression using a generalized class of skewed distributions

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Morales, Christian Galarza ; Davila, Victor Lachos ; Cabral, Celso Barbosa ; Cepero, Luis Castro
Total Authors: 4
Document type: Journal article
Source: STAT; v. 6, n. 1, p. 113-130, 2017.
Web of Science Citations: 0

It is well known that the widely popular mean regression model could be inadequate if the probability distribution of the observed responses do not follow a symmetric distribution. To deal with this situation, the quantile regression turns to be a more robust alternative for accommodating outliers and the misspecification of the error distribution because it characterizes the entire conditional distribution of the outcome variable. This paper presents a likelihood-based approach for the estimation of the regression quantiles based on a new family of skewed distributions. This family includes the skewed version of normal, Student-t, Laplace, contaminated normal and slash distribution, all with the zero quantile property for the error term and with a convenient and novel stochastic representation that facilitates the implementation of the expectation-maximization algorithm for maximum likelihood estimation of the pth quantile regression parameters. We evaluate the performance of the proposed expectation-maximization algorithm and the asymptotic properties of the maximum likelihood estimates through empirical experiments and application to a real-life dataset. The algorithm is implemented in the R package lqr, providing full estimation and inference for the parameters as well as simulation envelope plots useful for assessing the goodness of fit. Copyright (C) 2017 John Wiley \& Sons, Ltd. (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
FAPESP's process: 14/02938-9 - Estimation and diagnostics for censored mixed effects models using scale mixtures of skew-normal distributions
Grantee:Víctor Hugo Lachos Dávila
Support type: Regular Research Grants