Original title: Distributed Sequential Zero-Inflated Poisson Regression
Authors: Žemlička, R. ; Dedecius, Kamil
Document type: Research reports
Year: 2021
Language: eng
Series: Research Report, volume: 2393
Abstract: 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.\n
Keywords: GLM; Poisson regression; zero inflation

Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://library.utia.cas.cz/separaty/2021/AS/dedecius-0549265.pdf
Original record: http://hdl.handle.net/11104/0325721

Permalink: http://www.nusl.cz/ntk/nusl-508274


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