An integrated modeling framework for multivariate poisson process with temporal and spatial correlations

Cang Wu; Shubin Si

doi:10.1109/IEEM45057.2020.9309932

An integrated modeling framework for multivariate poisson process with temporal and spatial correlations

Cang Wu, Shubin Si

School of Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Multivariate Poisson (MP) counts are common in the course of manufacturing and service process. It is significant to monitor the MP counts and judge whether the process is in control or not. Most of the previous researches assumed that the variables of each univariate Poisson process are independent. Taking the temporal and spatial correlations into account, this article proposes an integrated model based on copula model and autoregressive (AR) process. Furthermore, the inference functions for margins (IFM) method and the expectation maximization (EM) algorithm accompanied by sequential importance resampling (SIR) method, provide satisfactory estimators in the proposed model.

Original language	English
Title of host publication	2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020
Publisher	IEEE Computer Society
Pages	1241-1245
Number of pages	5
ISBN (Electronic)	9781538672204
DOIs	https://doi.org/10.1109/IEEM45057.2020.9309932
State	Published - 14 Dec 2020
Event	2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020 - Virtual, Singapore, Singapore Duration: 14 Dec 2020 → 17 Dec 2020

Publication series

Name	IEEE International Conference on Industrial Engineering and Engineering Management
Volume	2020-December
ISSN (Print)	2157-3611
ISSN (Electronic)	2157-362X

Conference

Conference	2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020
Country/Territory	Singapore
City	Virtual, Singapore
Period	14/12/20 → 17/12/20

Keywords

Autoregressive
Copula
Expectation maximization
Multivariate Poisson
Sequential importance resampling

Access to Document

10.1109/IEEM45057.2020.9309932

Cite this

Wu, C., & Si, S. (2020). An integrated modeling framework for multivariate poisson process with temporal and spatial correlations. In 2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020 (pp. 1241-1245). Article 9309932 (IEEE International Conference on Industrial Engineering and Engineering Management; Vol. 2020-December). IEEE Computer Society. https://doi.org/10.1109/IEEM45057.2020.9309932

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title = "An integrated modeling framework for multivariate poisson process with temporal and spatial correlations",

abstract = "Multivariate Poisson (MP) counts are common in the course of manufacturing and service process. It is significant to monitor the MP counts and judge whether the process is in control or not. Most of the previous researches assumed that the variables of each univariate Poisson process are independent. Taking the temporal and spatial correlations into account, this article proposes an integrated model based on copula model and autoregressive (AR) process. Furthermore, the inference functions for margins (IFM) method and the expectation maximization (EM) algorithm accompanied by sequential importance resampling (SIR) method, provide satisfactory estimators in the proposed model.",

keywords = "Autoregressive, Copula, Expectation maximization, Multivariate Poisson, Sequential importance resampling",

author = "Cang Wu and Shubin Si",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020 ; Conference date: 14-12-2020 Through 17-12-2020",

year = "2020",

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doi = "10.1109/IEEM45057.2020.9309932",

language = "英语",

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Wu, C & Si, S 2020, An integrated modeling framework for multivariate poisson process with temporal and spatial correlations. in 2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020., 9309932, IEEE International Conference on Industrial Engineering and Engineering Management, vol. 2020-December, IEEE Computer Society, pp. 1241-1245, 2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020, Virtual, Singapore, Singapore, 14/12/20. https://doi.org/10.1109/IEEM45057.2020.9309932

An integrated modeling framework for multivariate poisson process with temporal and spatial correlations. / Wu, Cang; Si, Shubin.
2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020. IEEE Computer Society, 2020. p. 1241-1245 9309932 (IEEE International Conference on Industrial Engineering and Engineering Management; Vol. 2020-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - Multivariate Poisson (MP) counts are common in the course of manufacturing and service process. It is significant to monitor the MP counts and judge whether the process is in control or not. Most of the previous researches assumed that the variables of each univariate Poisson process are independent. Taking the temporal and spatial correlations into account, this article proposes an integrated model based on copula model and autoregressive (AR) process. Furthermore, the inference functions for margins (IFM) method and the expectation maximization (EM) algorithm accompanied by sequential importance resampling (SIR) method, provide satisfactory estimators in the proposed model.

AB - Multivariate Poisson (MP) counts are common in the course of manufacturing and service process. It is significant to monitor the MP counts and judge whether the process is in control or not. Most of the previous researches assumed that the variables of each univariate Poisson process are independent. Taking the temporal and spatial correlations into account, this article proposes an integrated model based on copula model and autoregressive (AR) process. Furthermore, the inference functions for margins (IFM) method and the expectation maximization (EM) algorithm accompanied by sequential importance resampling (SIR) method, provide satisfactory estimators in the proposed model.

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Wu C, Si S. An integrated modeling framework for multivariate poisson process with temporal and spatial correlations. In 2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020. IEEE Computer Society. 2020. p. 1241-1245. 9309932. (IEEE International Conference on Industrial Engineering and Engineering Management). doi: 10.1109/IEEM45057.2020.9309932

An integrated modeling framework for multivariate poisson process with temporal and spatial correlations

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