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Longitudinal categorical data analysis: a focus on Markov transition models

Grant number: 15/02628-2
Support type:Scholarships abroad - Research
Effective date (Start): August 01, 2015
Effective date (End): July 31, 2016
Field of knowledge:Physical Sciences and Mathematics - Probability and Statistics
Principal Investigator:Idemauro Antonio Rodrigues de Lara
Grantee:Idemauro Antonio Rodrigues de Lara
Host: John Philip Hinde
Home Institution: Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil
Local de pesquisa : National University of Ireland, Galway (NUI Galway), Ireland  

Abstract

The longitudinal categorical data analysis is an area of study that requires special attention, not only by the nature of the response variable but also the need to consider the possible correlation between repeated measures. There are three classes of models that can be employed: marginal models, mixed effects models and transition models. But there is a much larger approach to the classes of marginal and mixed effects models. The transition models are based on stochastic processes and, in categorical response variables, the interest is to model probabilities of categories transitions of individuals in time. Despite its functionality, these models are not generally used in the analysis of longitudinal data, due to their assumptions, number of parameters involved in the estimation process, difficulty in interpreting the results, among others. In this context, this project aims to review the three classes of models and give emphasis to the development and application of methods related to the transition models that can resolve issues related to stationary and the chain reach. The methodological procedures are focused on maximum likelihood and computational implementation theory will be on the software R. (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)
RODRIGUES DE LARA, IDEMAURO ANTONIO; HINDE, JOHN; TACONELI, CESAR AUGUSTO. Global and local tests to assess stationarity of Markov transition models. COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, v. 48, n. 4, p. 1019-1039, APR 21 2019. Web of Science Citations: 0.
DE LARA, I. A. R.; HINDE, J. P.; DE CASTRO, A. C.; DA SILVA, I. J. O. A proportional odds transition model for ordinal responses with an application to pig behaviour. Journal of Applied Statistics, v. 44, n. 6, p. 1031-1046, MAY 2017. Web of Science Citations: 0.
RODRIGUES DE LARA, IDEMAURO ANTONIO; HINDE, JOHN; TACONELI, CESAR AUGUSTO. An alternative method for evaluating stationarity in transition models. JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, v. 87, n. 15, p. 2962-2980, 2017. Web of Science Citations: 0.

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