Modern computational methods in stochastic modeling

• Grant number: 14/11831-3
• Support type: Research Grants - Visiting Researcher Grant - International
• Duration: September 01, 2014 - October 31, 2014
• Field of knowledge: Physical Sciences and Mathematics - Probability and Statistics - Statistics
• Principal researcher: Víctor Hugo Lachos Dávila
• Grantee: Víctor Hugo Lachos Dávila
• Visiting researcher: Jacek Leskow
• Visiting researcher institution: Wroclaw University of Technology, Poland
• Home Institution: Instituto de Matemática, Estatística e Computação Científica (IMECC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil

Abstract

In recent years, the analysis of survival data, time series data or heavy tailed data can be significantly improved with the techniques based on resampling. In each category of the modelling problems a fundamental question is to be able to better study the finite-sample distributions of the introduced estimators. In the following description of research activities, three very popular models will be considered: a point process nonparametric model for survival data, time series model for no stationary signals an inferential models for heavy tailed distributions. (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)

GARAY, ALDO M.; CASTRO, LUIS M.; LESKOW, JACEK; LACHOS, VICTOR H. Censored linear regression models for irregularly observed longitudinal data using the multivariate-t distribution. STATISTICAL METHODS IN MEDICAL RESEARCH, v. 26, n. 2, p. 542-566, APR 2017. Web of Science Citations: 7.