Parametric and semi-parametric regression models under the class of scale mixtures of normal distributions

• Grant number: 20/16713-0
• Support Opportunities: Regular Research Grants
• Start date: July 01, 2021
• End date: September 30, 2023
• Field of knowledge: Physical Sciences and Mathematics - Probability and Statistics - Statistics
• Principal Investigator: Caio Lucidius Naberezny Azevedo
• Grantee: Caio Lucidius Naberezny Azevedo
• Host Institution: Instituto de Matemática, Estatística e Computação Científica (IMECC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Associated researchers: Caio Lucidius Naberezny Azevedo ; Larissa Avila Matos

Abstract

Parametric and semi-parametric regression models represent two major areas in the modelingcontext, which have found applications in different areas. The aim of this project is to consider regression models in the presence of random effects,which are distributed under the family of scale mixture of skew-normal distributions (Branco and Dey 2001), that are effectively used in robust estimation in the presence of extreme observations.The main regression models that will be considered are: i) mixed linear model, ii) measurementerror model and iii) regression model of Item Response Theory (IRT).For the regression models (mixed linear model and measurement error model) uncensoredand for censored data, estimation and diagnostic analysis studies are considered, under parametricand semi-parametric structures. The semi-parametric regression refers to the flexibleincorporation of non-linear functional relationships into regression analysis. On the other hand,regression models in IRT, used more frequently in latent trait evaluations psychometric tests, theproposed models are based on asymmetric centered distributions. The estimation is consideredfrom a frequentist and Bayesian viewpoints. (AU)

Scientific publications

(8)

(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)

VALERIANO, KATHERINE A. L.; GALARZA, CHRISTIAN E.; MATOS, LARISSA A.; LACHOS, VICTOR H.. Likelihood-based inference for the multivariate skew-t regression with censored or missing responses. JOURNAL OF MULTIVARIATE ANALYSIS, v. 196, p. 22-pg., 2023-03-11. (20/16713-0)

VILCA, FILIDOR; VILA, ROBERTO; SAULO, HELTON; SANCHEZ, LUIS; LEAO, JEREMIAS. Theoretical results and modeling under the discrete Birnbaum-Saunders distribution. COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, v. N/A, p. 15-pg., 2022-08-07. (20/16713-0)

MATTOS, THALITA B.; LACHOS, VICTOR H.; CASTRO, LUIS M.; MATOS, LARISSA A.. Extending multivariate Student's-t semiparametric mixed models for longitudinal data with censored responses and heavy tails. STATISTICS IN MEDICINE, v. N/A, p. 24-pg., 2022-05-20. (20/16713-0)

GALARZA, CHRISTIAN E.; MATOS, LARISSA A.; LACHOS, VICTOR H.. An EM algorithm for estimating the parameters of the multivariate skew-normal distribution with censored responses. METRON-INTERNATIONAL JOURNAL OF STATISTICS, v. N/A, p. 23-pg., 2022-01-28. (20/16713-0)

DE ALENCAR, FRANCISCO H. C.; MATOS, LARISSA A.; LACHOS, VICTOR H.. Finite Mixture of Censored Linear Mixed Models for Irregularly Observed Longitudinal Data. JOURNAL OF CLASSIFICATION, v. 39, n. 3, p. 24-pg., 2022-07-08. (20/16713-0)

VALERIANO, KATHERINE A. L.; GALARZA, CHRISTIAN E.; MATOS, LARISSA A.. Moments and random number generation for the truncated elliptical family of distributions. STATISTICS AND COMPUTING, v. 33, n. 1, p. 20-pg., 2023-02-01. (20/16713-0)

GALARZA, CHRISTIAN E.; MATOS, LARISSA A.; CASTRO, LUIS M.; LACHOS, VICTOR H.. Moments of the doubly truncated selection elliptical distributions with emphasis on the unified multivariate skew-t distribution. JOURNAL OF MULTIVARIATE ANALYSIS, v. 189, p. 15-pg., 2022-01-03. (18/11580-1, 15/17110-9, 20/16713-0)

VILCA, FILIDOR; ZELLER, CAMILA BORELLI; BALAKRISHNAN, NARAYANASWAMY. Multivariate Birnbaum-Saunders distribution based on a skewed distribution and associated EM-estimation. BRAZILIAN JOURNAL OF PROBABILITY AND STATISTICS, v. 37, n. 1, p. 29-pg., 2023-03-01. (22/06421-7, 20/16713-0)