Prediction of Multimodal Poisson Variable using Discretization of Gaussian Data

0544576 - ÚTIA 2022 RIV PT eng C - Conference Paper (international conference)
Uglickich, Evženie - Nagy, Ivan - Petrouš, Matej
Prediction of Multimodal Poisson Variable using Discretization of Gaussian Data.
Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics. Setúbal: Scitepress, 2021 - (Gusikhin, O.; Nijmeijer, H.; Madani, K.), s. 600-608. ISBN 978-989-758-522-7. ISSN 2184-2809.
[International Conference on Informatics in Control, Automation and Robotics 2021 /18./. Setúbal (online) (PT), 06.07.2021-08.07.2021]
R&D Projects: GA MŠMT(CZ) 8A19009
Institutional support: RVO:67985556
Keywords : Poisson Distribution Prediction * Discrete Data * Discretization * Mixture based Clustering * Bayesian Recursive Mixture Estimation
OECD category: Statistics and probability
http://library.utia.cas.cz/separaty/2021/ZS/uglickich-0544576.pdf

The paper deals with predicting a discrete target variable described by the Poisson distribution based on the discretized Gaussian explanatory data under condition of the multimodality of a system observed. The discretization is performed using the recursive mixture-based clustering algorithms under Bayesian methodology. The proposed approach allows to estimate the Gaussian and Poisson models existing for each discretization interval of explanatory data and use them for the prediction. The main contributions of the approach include: (i) modeling the Poisson variable based on the cluster analysis of explanatory continuous data, (ii) the discretization approach based on recursive mixture estimation theory, (iii) the online prediction of the Poisson variable based on available Gaussian data discretized in real time. Results of illustrative experiments and comparison with the Poisson regression is demonstrated.
Permanent Link: http://hdl.handle.net/11104/0321816