Sequential Change-point Detection for Binomial Time Series
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866915483448508416 |
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| author | Liu, Yajun Andrews, Beth |
| author_facet | Liu, Yajun Andrews, Beth |
| contents | A binomial time series describes binary behaviors of individuals within a group, which depend on group behaviors in the past. Binomial time series data is widely applied in fields such as infection tracking and behavior analysis. In this paper, we introduce a generalized Binomial AR($p$) model with exogenous variables based on Generalized Linear Model (GLM), prove the statistical properties of the model when $p = 1$, and provide a parameter estimation method. Then, we propose a sequential change-point detection method for the generalized Binomial AR(1) model, facilitate real-time data monitoring and triggering alarms when a change point is detected. We apply the generalized Binomial AR(1) model to weekly pneumonia \& influenza mortality data and successfully identify change points related to the COVID-19 outbreak using the proposed method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_17274 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Sequential Change-point Detection for Binomial Time Series Liu, Yajun Andrews, Beth Methodology A binomial time series describes binary behaviors of individuals within a group, which depend on group behaviors in the past. Binomial time series data is widely applied in fields such as infection tracking and behavior analysis. In this paper, we introduce a generalized Binomial AR($p$) model with exogenous variables based on Generalized Linear Model (GLM), prove the statistical properties of the model when $p = 1$, and provide a parameter estimation method. Then, we propose a sequential change-point detection method for the generalized Binomial AR(1) model, facilitate real-time data monitoring and triggering alarms when a change point is detected. We apply the generalized Binomial AR(1) model to weekly pneumonia \& influenza mortality data and successfully identify change points related to the COVID-19 outbreak using the proposed method. |
| title | Sequential Change-point Detection for Binomial Time Series |
| topic | Methodology |
| url | https://arxiv.org/abs/2402.17274 |