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

• Grant number: 18/05013-7
• Support Opportunities: Research Grants - Visiting Researcher Grant - International
• Start date: June 25, 2018
• End date: August 20, 2018
• Field of knowledge: Physical Sciences and Mathematics - Probability and Statistics - 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
• Host Institution: Instituto de Matemática, Estatística e Computação Científica (IMECC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• City of the host institution: Campinas

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

(5)

(The scientific publications listed on this page originate from the Web of Science or SciELO databases. Their authors have cited FAPESP grant or fellowship project numbers awarded to Principal Investigators or Fellowship Recipients, whether or not they are among the authors. This information is collected automatically and retrieved directly from those bibliometric databases.)

VALERIANO, KATHERINE A. L.; LACHOS, VICTOR H.; PRATES, MARCOS O.; MATOS, LARISSA A.. Likelihood-based inference for spatiotemporal data with censored and missing responses. ENVIRONMETRICS, v. 32, n. 3, DEC 2020. (18/05013-7)

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. (18/05013-7, 16/05420-6)

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. (15/20922-5, 18/05013-7)

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. (15/20922-5, 18/05013-7, 15/05385-3, 11/22063-9)

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. (15/05385-3, 18/05013-7, 14/02938-9, 11/22063-9)