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New mixed binomial regression models to unbalancing data and extensions

Grant number: 17/07773-6
Support type:Regular Research Grants
Duration: June 01, 2017 - May 31, 2019
Field of knowledge:Physical Sciences and Mathematics - Probability and Statistics
Principal Investigator:Vicente Garibay Cancho
Grantee:Vicente Garibay Cancho
Home Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil


We introduce generalized links function for the modelling of binomial and binary responses which can be appropriate when the probability of a given binary response approaches 0 at a different rate than it approaches 1. It is to unbalancing data set. The proposal is based on exponentiated versions of distributions of base and their corresponding reverse distributions. As special case, known links are obtained. Properties of the proposed links are presented. Maximum Likelihood and Bayesian MCMC inference approaches are developed. Extensions of this models to ordinal regression models, mixed regression model and item response theory are considered too. Applications and simulation studies are also presented, showing the advantages of the proposal over commonly used models. (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)
LEMONTE, ARTUR J.; BAZAN, JORGE L. New links for binary regression: an application to coca cultivation in Peru. TEST, v. 27, n. 3, p. 597-617, SEP 2018. Web of Science Citations: 1.

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