Journal article
Statistical monitoring of over-dispersed multivariate count data using approximate likelihood ratio tests
International journal of production research, Vol.54(21), pp.6579-6593
11/01/2016
DOI: 10.1080/00207543.2015.1126373
Abstract
In this paper, we develop a statistical monitoring scheme for multivariate count data. In many applications involving multivariate count data, individual variables are not only correlated to each other, but also over-dispersed. Traditional statistical monitoring methods for multivariate count data that assume simple statistical models fail to fit the data collected when the underlying process is under normal working state, also referred to as the in-control state. Therefore, we propose a monitoring scheme which is based on the Poisson-multivariate Gaussian mixed model. Although such models are quite flexible, efficient statistical monitoring schemes for such models have not been developed. In this paper, we develop likelihood ratio test-based monitoring schemes that are shown to be superior to standard multivariate statistical monitoring schemes. The key challenge in developing likelihood ratio test for the Poisson-multivariate Gaussian mixed models is that the likelihood function can only be calculated by multidimensional numerical integration. We tackle this issue using an approximation of this complex likelihood function.
Details
- Title: Subtitle
- Statistical monitoring of over-dispersed multivariate count data using approximate likelihood ratio tests
- Creators
- Devashish Das - University of Wisconsin–MadisonShiyu Zhou - University of Wisconsin–MadisonYong Chen - University of IowaJohn Horst - National Institute of Standards and Technology
- Resource Type
- Journal article
- Publication Details
- International journal of production research, Vol.54(21), pp.6579-6593
- Publisher
- Taylor & Francis
- DOI
- 10.1080/00207543.2015.1126373
- ISSN
- 0020-7543
- eISSN
- 1366-588X
- Grant note
- CMMI-1161350 / NSF (10.13039/100000001) IIS-1343974 / NSF (10.13039/100000001)
- Language
- English
- Date published
- 11/01/2016
- Academic Unit
- Industrial and Systems Engineering
- Record Identifier
- 9984186952402771
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