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Nonlinear mixed-effects models with multiple censored responses using heavy-tailed distributions

Grant number: 16/05420-6
Support type:Scholarships in Brazil - Post-Doctorate
Effective date (Start): July 01, 2016
Effective date (End): March 07, 2017
Field of knowledge:Physical Sciences and Mathematics - Probability and Statistics
Cooperation agreement: Coordination of Improvement of Higher Education Personnel (CAPES)
Principal Investigator:Víctor Hugo Lachos Dávila
Grantee:Larissa Avila Matos
Home Institution: Instituto de Matemática, Estatística e Computação Científica (IMECC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil

Abstract

This project will present a new approach for estimation and influence diagnostics in nonlinear mixed-effects models with multiple censored responses using scale mixtures of normal distributions from a classical and Bayesian perspective. In the framework of linear and non-linear mixed-effects models withmultiple censored responses (MLMEC/MNLMEC), the random effects and residual errors are routinely assumed to have a normal distribution, mainly for mathematical convenience. However, this methodhas been criticized in the literature due to its sensitivity to deviations from the normality assumption Wang et al. (2015). In this project, we will assume that the random effects and errors jointly follow amultivariate scale mixture of normal distributions. The Student-t, slash and contaminated normal, among others distributions, are contained in this class and provide interesting alternatives to the Gaussian model, which produce robust estimates against outlying observations. We will propose an exact estimation procedure to obtain the maximum likelihood estimates of the model parameters of this complex structure,using a stochastic approximation of the traditional EM algorithm proposed by Dempster et al. (1977), called SAEM (Delyon et al., 1999). The main goal is to extend the work of Lachos et al. (2011), Matos et al. (2015), Wang et al. (2015) and Matos et al. (2016). (AU)

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)
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.; MATOS, LARISSA A.; BARBOSA, THAIS S.; GARAY, ALDO M.; DEY, DIPAK K. Influence diagnostics in spatial models with censored response. ENVIRONMETRICS, v. 28, n. 7 NOV 2017. Web of Science Citations: 0.

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