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Semiparametric mixed effects models with multiple censored response using scale mixtures of normal distributions

Grant number: 18/05013-7
Support type:Research Grants - Visiting Researcher Grant - International
Duration: June 25, 2018 - August 20, 2018
Field of knowledge:Physical Sciences and Mathematics - Probability and Statistics
Principal Investigator:Larissa Avila Matos
Grantee:Larissa Avila Matos
Visiting researcher: Victor Hugo Lachos Davila
Visiting researcher institution: University of Connecticut (UCONN), United States
Home Institution: Instituto de Matemática, Estatística e Computação Científica (IMECC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil

Abstract

Semiparametric regression refers to the flexible incorporation of nonlinear functional relationships into regression analysis. Any application area that uses regression analysis may benefit from semiparametric regression. In this context, the main goal is to expand the censored regression models considering, on the one hand, that the response variable is linearly dependent on some variables, while its relationship with the other variables is characterized by non-parametric functions and, on the other hand, the random terms of the regression model belong to a class of symmetrical heavy tail distributions capable of accommodating extreme and/or influential observations in a better way than the normal distribution. (AU)

Scientific publications (4)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
MATOS, LARISSA A.; LACHOS, VICTOR H.; LIN, TSUNG-I; CASTRO, LUIS M. Heavy-tailed longitudinal regression models for censored data: a robust parametric approach. TEST, v. 28, n. 3, p. 844-878, SEP 2019. Web of Science Citations: 0.
LACHOS, VICTOR H.; MATOS, LARISSA A.; CASTRO, LUIS M.; CHEN, MING-HUI. Flexible longitudinal linear mixed models for multiple censored responses data. STATISTICS IN MEDICINE, v. 38, n. 6, p. 1074-1102, MAR 15 2019. Web of Science Citations: 0.
LACHOS, VICTOR H.; CABRAL, CELSO R. B.; PRATES, MARCOS O.; DEY, DIPAK K. Flexible regression modeling for censored data based on mixtures of student-t distributions. Computational Statistics, v. 34, n. 1, p. 123-152, MAR 2019. Web of Science Citations: 0.
MATOS, LARISSA A.; CASTRO, LUIS M.; CABRAL, CELSO R. B.; LACHOS, VICTOR H. Multivariate measurement error models based on Student-t distribution under censored responses. STATISTICS, v. 52, n. 6, p. 1395-1416, 2018. Web of Science Citations: 0.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.