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Distributed Sequential Zero-Inflated Poisson Regression
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SYSNO ASEP 0549265 Document Type V - Research Report R&D Document Type The record was not marked in the RIV Title Distributed Sequential Zero-Inflated Poisson Regression Author(s) Žemlička, R. (CZ)
Dedecius, Kamil (UTIA-B) RID, ORCIDNumber of authors 2 Issue data Praha: ÚTIA AV ČR, v. v. i.,, 2021 Series Research Report Series number 2393 Number of pages 11 s. Publication form Print - P Language eng - English Country CZ - Czech Republic Keywords Poisson regression ; zero inflation ; GLM Subject RIV BD - Theory of Information OECD category Applied mathematics Institutional support UTIA-B - RVO:67985556 Annotation The zero-inflated Poisson regression model is a generalized linear model (GLM) for non-negative count variables with an excessive number of zeros. This letter proposes its low-cost distributed sequential inference from streaming data in networks with information diffusion. The model is viewed as a probabilistic mixture of a Poisson and a zero-located Dirac component, whose probabilities are estimated using a quasi-Bayesian procedure. The regression coefficients are inferred by means of a weighted Bayesian update. The network nodes share their posterior distributions using the diffusion protocol.
Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2022
Number of the records: 1