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Please use this identifier to cite or link to this item: https://elib.bsu.by/handle/123456789/233401
Title: Implementation of generalized additive models for spatial beta regression
Authors: Zikariene, E.
Ducinskas, K.
Keywords: ЭБ БГУ::ЕСТЕСТВЕННЫЕ И ТОЧНЫЕ НАУКИ::Математика
ЭБ БГУ::ЕСТЕСТВЕННЫЕ И ТОЧНЫЕ НАУКИ::Кибернетика
Issue Date: 2019
Publisher: Minsk : BSU
Citation: Computer Data Analysis and Modeling: Stochastics and Data Science : Proc. of the Twelfth Intern. Conf., Minsk, Sept. 18-22, 2019. – Minsk : BSU, 2019. – P. 341-343.
Abstract: Beta regression models are proposed by to model the continuous variates that assume values in the standard unit interval, e.g. rates, proportions or concentration, or inequality indices. These models belong to the class of generalized linear mixed models (GLM) or more general generalized additive models (GAM) with responses belonging to the exponential family. In present study we use GAM to model spatial Beta data. We develop the Monte Carlo version of EM algorithm for obtaining of penalized maximum likelihood estimators of model parameters. This method is applied to real data set on Black carrageen concentration index to obtain a model of its distribution over the southeastern Baltic Sea
URI: http://elib.bsu.by/handle/123456789/233401
ISBN: 978-985-566-811-5
Appears in Collections:2019. Computer Data Analysis and Modeling : Stochastics and Data Science

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