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Multiple modification points in discrete data models

Grant number: 19/22412-5
Support type:Scholarships abroad - Research
Effective date (Start): February 02, 2020
Effective date (End): February 01, 2021
Field of knowledge:Physical Sciences and Mathematics - Probability and Statistics - Statistics
Principal researcher:Katiane Silva Conceição
Grantee:Katiane Silva Conceição
Host: Nalini Ravishanker
Home Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
Research place: University of Connecticut (UCONN), United States  


Count datasets are common in many areas of knowledge and, consequently, more general discrete distributions have been proposed due to the unique characteristics of each set. In particular, count data may present discrepancies (greater or lesser) in the observed frequencies of two observations, said k1 and k2, by comparing them with their expected frequencies obtained from a particular discrete distribution. In this sense, a modification in the probability mass function of discrete distributions is essential to adequately explain the behavior of the data. Following this context, the main objective of this project is to propose the family of discrete distributions k1 and k2 modified, which are able to model data sets that present or not some kind of modification (inflation and/or deflation) in the frequency of observations k1 and k2. (AU)

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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)
CONCEICAO, KATIANE S.; SUZUKI, ADRIANO K.; ANDRADE, MARINHO G. A Bayesian approach for zero-modified Skellam model with Hamiltonian MCMC. Statistical Methods and Applications, JUL 2020. Web of Science Citations: 0.

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