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Mixed and mixture regression models for complex problems

Abstract

This Project has application in several problems related to mixed and mixture regression models with applications in different areas. The Project consists of 4 subprojects, involving the following studies: (1) incorporation of functional covariates in Semi-Parametric Regression models for unsupervised learning purposes and with application in two sets of data, one of them associated with a clinical trial study with patients with depression and another with information on the health status of dairy cows, (2) study the properties of the estimators of the variance parameters and the predictors of random effects in genetic selection improvement based on mixed linear models, (3) compare conditional and marginal models for zero-inflated multivariate count data, with an application to a dolphin food consumption dataset, (4) study the Birnbaum-Saunders Bayesian mixed model for the analysis of zero-augmented repeated measure data with heavy tails, with application to a dataset on food habitual consumption. (AU)

Articles published in Agência FAPESP Newsletter about the research grant:
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VEICULO: TITULO (DATA)
VEICULO: TITULO (DATA)

Scientific publications
(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)
GARCIA, NANCY L.; RODRIGUES-MOTTA, MARIANA; MIGON, HELIO S.; PETKOVA, EVA; TARPEY, THADDEUS; OGDEN, R. TODD; GIORDANO, JULIO O.; PEREZ, MARTIN M.. Unsupervised Bayesian classification for models with scalar and functional covariates. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, v. 73, n. 3, p. 24-pg., . (23/00592-7, 17/15306-9, 19/10800-0, 18/06811-4)