Adaptive CUSUM Chart for Real-Time Monitoring of Bivariate Event Data
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| Format: | Preprint |
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2025
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| _version_ | 1866915472283271168 |
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| author | Parakulum, Gokul Li, Jun |
| author_facet | Parakulum, Gokul Li, Jun |
| contents | Monitoring time-between-events (TBE) data, where the goal is to track the time between consecutive events, has important applications across various fields. Many existing schemes for monitoring multivariate TBE data suffer from inherent delays, as they require waiting until all components of the observation vector are available before making inferences about the process state. In practice, however, these components are rarely recorded simultaneously. To address this issue, Zwetsloot et al. proposed a Shewhart chart for bivariate TBE data that updates the process status as individual observations arrive. However, like most Shewhart-type charts, their method evaluates the process based solely on the most recent observation and does not incorporate historical information. As a result, it is ineffective in detecting small to moderate changes. To overcome this limitation, we develop an adaptive CUSUM chart that updates with each incoming observation while also accumulating information over time. Simulation studies and real-data applications demonstrate that our method substantially outperforms the Shewhart chart of Zwetsloot et al., offering a robust and effective tool for real-time monitoring of bivariate TBE data. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_00535 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptive CUSUM Chart for Real-Time Monitoring of Bivariate Event Data Parakulum, Gokul Li, Jun Methodology Monitoring time-between-events (TBE) data, where the goal is to track the time between consecutive events, has important applications across various fields. Many existing schemes for monitoring multivariate TBE data suffer from inherent delays, as they require waiting until all components of the observation vector are available before making inferences about the process state. In practice, however, these components are rarely recorded simultaneously. To address this issue, Zwetsloot et al. proposed a Shewhart chart for bivariate TBE data that updates the process status as individual observations arrive. However, like most Shewhart-type charts, their method evaluates the process based solely on the most recent observation and does not incorporate historical information. As a result, it is ineffective in detecting small to moderate changes. To overcome this limitation, we develop an adaptive CUSUM chart that updates with each incoming observation while also accumulating information over time. Simulation studies and real-data applications demonstrate that our method substantially outperforms the Shewhart chart of Zwetsloot et al., offering a robust and effective tool for real-time monitoring of bivariate TBE data. |
| title | Adaptive CUSUM Chart for Real-Time Monitoring of Bivariate Event Data |
| topic | Methodology |
| url | https://arxiv.org/abs/2509.00535 |