Zago, Daniele and Capizzi, Giovanna (2023) Alternative parameter learning schemes for monitoring process stability. [Data Collection]
Collection description
In statistical process control, accurately estimating in-control (IC) parameters is crucial for effective monitoring. This typically requires a Phase I analysis to obtain estimates before monitoring commences. The traditional “fixed” estimate (FE) approach uses these estimates exclusively, while the “adaptive” estimate (AE) approach updates the estimates with each new observation. Such extreme criteria reflect the traditional bias-variance tradeoff in the framework of the sequential parameter learning schemes. This paper proposes an intermediate update rule that generalizes two ad hoc criteria for monitoring univariate Gaussian data, by giving a lower probability to parameter updates when an out-of-control (OC) situation is likely, therefore updating more frequently when there is no evidence of an OC scenario. The simulation study shows that this approach improves the detection power for small and early shifts, which are commonly regarded as a weakness of control charts based on fully online adaptive estimation. The paper also shows that the proposed method performs similarly to the fully adaptive procedure for larger or later shifts. The proposed method is illustrated by monitoring the sudden increase in ICU counts during the 2020 COVID outbreak in New York.
| DOI: |  10.6084/m9.figshare.24171168.v1 |
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| Keywords: | Space Science, Genetics, FOS: Biological sciences, Biotechnology, Mathematical Sciences not elsewhere classified | |||||||||
| Subjects: | Physical:Sciences and Engineering > Mathematics: All areas of mathematics, pure and applied, plus mathematical foundations of computer science, mathematical physics and statistics > Statistics | |||||||||
| Department: | Departments > Dipartimento di Scienze statistiche | |||||||||
| Depositing User: | Research Data Unipd | |||||||||
| Date Deposited: | 17 Apr 2024 13:02 | |||||||||
| Last Modified: | 18 Apr 2024 11:14 | |||||||||
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| Type of data: | Mixed | |||||||||
| Research funder: | UNIPD DOR2021 | |||||||||
| Collection period: |
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| Resource language: | it | |||||||||
| Metadata language: | it | |||||||||
| Publisher: | Taylor & Francis | |||||||||
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| Date: | 2023 | |||||||||
| Copyright holders: | The Authors | |||||||||
| URI: | https://researchdata.cab.unipd.it/id/eprint/1270 |
orcid.org/0000-0003-0778-7099
