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

Result code in 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>
Alternative codes found
RIV/68407700:21260/23:00364216
Result on the web
<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>

Result language
angličtina
Original language name
Prediction of overdispersed count data using real-time cluster-based discretization of explanatory variables
Original language description
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.
Czech name
—
Czech description
—

Project
<a href="/en/project/8A19009" target="_blank" >8A19009: Arrowhead Tools for Engineering of Digitalisation Solutions</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Book/collection name
Informatics in Control, Automation and Robotics. ICINCO 2021 : Revised Selected Papers
ISBN
978-3-031-26474-0
Number of pages of the result
22
Pages from-to
163-184
Number of pages of the book
209
Publisher name
Springer
Place of publication
Cham
UT code for WoS chapter
—

Similar results(10)