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

机电学院

Northwestern Polytechnical University Xian
Lanzhou University of Technology

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

摘要

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.

源语言	英语
主期刊名	2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020
出版商	IEEE Computer Society
页	1241-1245
页数	5
ISBN（电子版）	9781538672204
DOI	https://doi.org/10.1109/IEEM45057.2020.9309932
出版状态	已出版 - 14 12月 2020
活动	2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020 - Virtual, Singapore, 新加坡期限: 14 12月 2020 → 17 12月 2020

丛书

姓名	IEEE International Conference on Industrial Engineering and Engineering Management
卷	2020-December
ISSN（印刷版）	2157-3611
ISSN（电子版）	2157-362X

会议

会议	2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020
国家/地区	新加坡
市	Virtual, Singapore
时期	14/12/20 → 17/12/20

访问文件

10.1109/IEEM45057.2020.9309932

其它文件与链接

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引用此

Wu, C., & Si, S. (2020). An integrated modeling framework for multivariate poisson process with temporal and spatial correlations. 在 2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020 (页码 1241-1245). 期刊论文 9309932 (IEEE International Conference on Industrial Engineering and Engineering Management; 卷 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",

month = dec,

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doi = "10.1109/IEEM45057.2020.9309932",

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Wu, C & Si, S 2020, An integrated modeling framework for multivariate poisson process with temporal and spatial correlations. 在 2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020., 9309932, IEEE International Conference on Industrial Engineering and Engineering Management, 卷 2020-December, IEEE Computer Society, 页码 1241-1245, 2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020, Virtual, Singapore, 新加坡, 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. 页码 1241-1245 9309932 (IEEE International Conference on Industrial Engineering and Engineering Management; 卷 2020-December).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

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AU - Wu, Cang

AU - Si, Shubin

PY - 2020/12/14

Y1 - 2020/12/14

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.

KW - Autoregressive

KW - Copula

KW - Expectation maximization

KW - Multivariate Poisson

KW - Sequential importance resampling

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DO - 10.1109/IEEM45057.2020.9309932

M3 - 会议稿件

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T3 - IEEE International Conference on Industrial Engineering and Engineering Management

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BT - 2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020

PB - IEEE Computer Society

T2 - 2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020

Y2 - 14 December 2020 through 17 December 2020

ER -

Wu C, Si S. An integrated modeling framework for multivariate poisson process with temporal and spatial correlations. 在 2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020. IEEE Computer Society. 2020. 页码 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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