Informatics in Control, Automation and Robotics. ICINCO 2021 : Revised Selected Papers

0569490 - ÚTIA 2024 RIV CH eng M - Monography Chapter
Uglickich, Evženie - Nagy, Ivan
Prediction of overdispersed count data using real-time cluster-based discretization of explanatory variables.
Informatics in Control, Automation and Robotics. ICINCO 2021 : Revised Selected Papers. Cham: Springer, 2023 - (Gusikhin, O.; Madani, K.; Nijmeijer, H.), s. 163-184. Lecture Notes in Electrical Engineering, 1006. ISBN 978-3-031-26474-0
R&D Projects: GA MŠMT(CZ) 8A19009
Institutional support: RVO:67985556
Keywords : Cluster-based model * Count data * Overdispersion * Recursive Bayesian mixture estimation
OECD category: Statistics and probability
http://library.utia.cas.cz/separaty/2023/ZS/uglickich-0569490.pdf

The chapter focuses on the description of the relationship of the count variable and explanatory Gaussian variables. The cluster-based model is proposed, which is constructed on conditionally independent Gaussian clusters captured in real time using recursive algorithms of the Bayesian mixture estimation theory. The resulting model is expected to be used for predicting count data using real time Gaussian observations. The Poisson distribution of the count data is used as a basic model. However, in reality, count data often do not satisfy the Poisson assumption of equal mean and variance. For this case, five cluster-based Poisson-related models of overdispersed data have been studied. The experimental part of the chapter demonstrates a comparison of the prediction accuracy of the considered models with two theoretical counterparts for the case of weak and strong overdispersion with the help of simulations. The paper reports that the most accurate prediction in average has been provided by the cluster-based Generalized Poisson models.
Permanent Link: https://hdl.handle.net/11104/0340877