Exponentially weighted moving average charts for correlated multivariate Poisson processes

被引：9

作者：

Akhundjanov, Sherzod B. ^{[1
]}

Pascual, Francis G. ^{[2
]}

机构：

[1] Utah State Univ, Dept Appl Econ, Logan, UT 84322 USA

[2] Washington State Univ, Dept Math & Stat, POB 643113, Pullman, WA 99164 USA

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2017年 / 46卷 / 10期

关键词：

Average run length; Covariance structure; EWMA; Hepatitis C; Multivariate Poisson distribution; Statistical process control; STATISTICAL PROCESS-CONTROL; COUNT DATA; DISTRIBUTIONS; REGRESSION;

D O I：

10.1080/03610926.2015.1096392

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

In this article, we study exponentially weighted moving average (EWMA) control schemes to monitor the multivariate Poisson distribution with a general covariance structure, so that the practitioner can simultaneously monitor multiple correlated attribute processes more effectively. The statistical performance of the charts is assessed in terms of the run length properties and compared against other mainstream attribute control schemes. The application of the proposed methods to real-life and simulated datasets is demonstrated.

引用

页码：4977 / 5000

页数：24

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