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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.)

A skew-t quantile regression for censored and missing data

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Galarza Morales, Christian E. [1] ; Lachos, Victor H. [2] ; Bourguignon, Marcelo [3]
Total Authors: 3
[1] Escuela Super Politecn Litoral, Fac Ciencias Nat & Matemat, Guayaquil - Ecuador
[2] Univ Connecticut, Dept Stat, Storrs, CT 06269 - USA
[3] Univ Rio Grande Norte, Dept Estat, Natal, RN - Brazil
Total Affiliations: 3
Document type: Journal article
Source: STAT; v. 10, n. 1 DEC 2021.
Web of Science Citations: 0

Quantile regression has emerged as an important analytical alternative to the classical mean regression model. However, the analysis could be complicated by the presence of censored measurements due to a detection limit of equipment in combination with unavoidable missing values arising when, for instance, a researcher is simply unable to collect an observation. Another complication arises when measures depart significantly from normality, for instance, in the presence of skew heavy-tailed observations. For such data structures, we propose a robust quantile regression for censored and/or missing responses based on the skew-t distribution. A computationally feasible EM-based procedure is developed to carry out the maximum likelihood estimation within such a general framework. Moreover, the asymptotic standard errors of the model parameters are explicitly obtained via the information-based method. We illustrate our methodology by using simulated data and two real data sets. (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: 18/11580-1 - Moments of doubly truncated multivariate distributions
Grantee:Christian Eduardo Galarza Morales
Support type: Scholarships abroad - Research Internship - Doctorate