Control Charts for Overdispersed Count Data: Exploring the Poisson Chris-Jerry Distribution in Agriculture and Medicine

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F25%3A10257561" target="_blank" >RIV/61989100:27740/25:10257561 - isvavai.cz</a>

Výsledek na webu

<a href="https://onlinelibrary.wiley.com/doi/10.1002/qre.3745?af=R" target="_blank" >https://onlinelibrary.wiley.com/doi/10.1002/qre.3745?af=R</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/qre.3745" target="_blank" >10.1002/qre.3745</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Control Charts for Overdispersed Count Data: Exploring the Poisson Chris-Jerry Distribution in Agriculture and Medicine

Popis výsledku v původním jazyce

Statistical process control (SPC) is vital for overseeing processes and ensuring quality standards, with control charts being key tools in this process. As manufacturing systems and components become more complex, there is an increasing demand for control charts built on advanced statistical distributions. The Poisson-based count chart is commonly used to monitor nonconformities in production, but its use depends on the assumption that the mean and variance of the data are equal. In many cases, particularly in fields such as biology and medicine, overdispersion occurs, where the variance surpasses the mean. In such cases, the Poisson Chris-Jerry (PSNCJ) distribution offers a more suitable approach for modeling count data. This study explores the development and characteristics of the PSNCJ distribution and presents control charts specifically designed for datasets that conform to this model. Its effectiveness is evaluated through both simulations and real-world applications. Moreover, the PSNCJ count chart is applied to datasets from agriculture and biology, demonstrating its practical significance in these fields. The results validate the utility and accuracy of the proposed control charts.

Název v anglickém jazyce

Control Charts for Overdispersed Count Data: Exploring the Poisson Chris-Jerry Distribution in Agriculture and Medicine

Popis výsledku anglicky

Statistical process control (SPC) is vital for overseeing processes and ensuring quality standards, with control charts being key tools in this process. As manufacturing systems and components become more complex, there is an increasing demand for control charts built on advanced statistical distributions. The Poisson-based count chart is commonly used to monitor nonconformities in production, but its use depends on the assumption that the mean and variance of the data are equal. In many cases, particularly in fields such as biology and medicine, overdispersion occurs, where the variance surpasses the mean. In such cases, the Poisson Chris-Jerry (PSNCJ) distribution offers a more suitable approach for modeling count data. This study explores the development and characteristics of the PSNCJ distribution and presents control charts specifically designed for datasets that conform to this model. Its effectiveness is evaluated through both simulations and real-world applications. Moreover, the PSNCJ count chart is applied to datasets from agriculture and biology, demonstrating its practical significance in these fields. The results validate the utility and accuracy of the proposed control charts.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

—

Návaznosti

O - Projekt operacniho programu

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Quality and Reliability Engineering International

ISSN

0748-8017

e-ISSN

1099-1638

Svazek periodika

41

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

23

Strana od-do

1913-1935

Kód UT WoS článku

001431327100001

EID výsledku v databázi Scopus

2-s2.0-85218700272