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Robust Estimation in Spatial Models for Censored Data

Grant number: 15/17110-9
Support type:Scholarships in Brazil - Doctorate
Effective date (Start): March 01, 2016
Effective date (End): February 29, 2020
Field of knowledge:Physical Sciences and Mathematics - Probability and Statistics - Applied Probability and Statistics
Principal researcher:Larissa Avila Matos
Grantee:Christian Eduardo Galarza Morales
Home Institution: Instituto de Matemática, Estatística e Computação Científica (IMECC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Associated scholarship(s):18/11580-1 - Moments of doubly truncated multivariate distributions, BE.EP.DR

Abstract

The goal of this project is to present a classical and Bayesian study in spatialmodels for censored data using more robust distributions than the normal andskew-normal distribution, i.e., using the scale mixture of skew-normal class of distributions. Furthermore, it will be present classical and Bayesian diagnostic studies based in local influence methods (Cook, 1986) and the q-divergence (Peng & Dey, 1995), respectively, as discuss in Lachos et al. (2011) and Lachos et al. (2013). For the estimation step, we will use EM (Expectation-Maximization), SAEM (Stochastic Approximation of the EM) and the Gibbs Sampler algorithms with implementation in R, C++ and WinBugs.The proposal of this project looks for contributing positively to the developing ofspatial models for censored data, providing new results in models of practical interest, extending and complementing some previous results found in Militino and Ugarte (1999); Kim and Mallick (2004);De Oliveira (2005);Fridley and Dixon (2006); Toscas (2010); Karimi and Mohammadzadeh (2012); Prates et al. (2012); Assumpção et al. (2014); Prates et al. (2014); Schelin and Sjostedt-de Luna (2014); De Bastianiet al. (2014), among others.

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Scientific publications (5)
(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)
GALARZA, CHRISTIAN E.; LIN, I, TSUNG-; WANG, WAN-LUN; LACHOS, VICTOR H. On moments of folded and truncated multivariate Student-t distributions based on recurrence relations. METRIKA, JAN 2021. Web of Science Citations: 0.
LEMUS, MARCELA NUNEZ; LACHOS, VICTOR H.; GALARZA, CHRISTIAN E.; MATOS, LARISSA A. Estimation and diagnostics for partially linear censored regression models based on heavy-tailed distributions. STATISTICS AND ITS INTERFACE, v. 14, n. 2, p. 165-182, 2021. Web of Science Citations: 0.
GALARZA, CHRISTIAN E.; CASTRO, LUIS M.; LOUZADA, FRANCISCO; LACHOS, VICTOR H. Quantile regression for nonlinear mixed effects models: a likelihood based perspective. STATISTICAL PAPERS, v. 61, n. 3, p. 1281-1307, JUN 2020. Web of Science Citations: 0.
GALARZA, CHRISTIAN E.; LACHOS, VICTOR H.; BANDYOPADHYAY, DIPANKAR. Quantile regression in linear mixed models: a stochastic approximation EM approach. STATISTICS AND ITS INTERFACE, v. 10, n. 3, p. 471-482, 2017. Web of Science Citations: 5.
MORALES, CHRISTIAN GALARZA; DAVILA, VICTOR LACHOS; CABRAL, CELSO BARBOSA; CEPERO, LUIS CASTRO. Robust quantile regression using a generalized class of skewed distributions. STAT, v. 6, n. 1, p. 113-130, 2017. Web of Science Citations: 0.

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