Autor(es)

Galarza Morales, Christian E. ; Lachos, Victor H. ; Bourguignon, Marcelo

• Tipo de documento: Artigo Científico
• Fonte: STAT; v. 10, n. 1, p. 15-pg., 2021-12-01.

Resumo

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)

• Processo FAPESP: 18/11580-1 - Momentos de distribuições multivariadas duplamente truncadas
• Beneficiário: Christian Eduardo Galarza Morales
• Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Doutorado
• Processo FAPESP: 15/17110-9 - Estimação Robusta em Modelos Espaciais para Dados Censurados.
• Beneficiário: Christian Eduardo Galarza Morales
• Modalidade de apoio: Bolsas no Brasil - Doutorado