Prediction of overdispersed count data using real-time cluster-based discretization of explanatory variables

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F23%3A00569490" target="_blank" >RIV/67985556:_____/23:00569490 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21260/23:00364216

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-031-26474-0_9" target="_blank" >http://dx.doi.org/10.1007/978-3-031-26474-0_9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-26474-0_9" target="_blank" >10.1007/978-3-031-26474-0_9</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Prediction of overdispersed count data using real-time cluster-based discretization of explanatory variables

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Prediction of overdispersed count data using real-time cluster-based discretization of explanatory variables

Popis výsledku anglicky

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.

Druh

C - Kapitola v odborné knize

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/8A19009" target="_blank" >8A19009: Arrowhead Tools for Engineering of Digitalisation Solutions</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název knihy nebo sborníku

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

ISBN

978-3-031-26474-0

Počet stran výsledku

22

Strana od-do

163-184

Počet stran knihy

209

Název nakladatele

Springer

Místo vydání

Cham

Kód UT WoS kapitoly

—