Nonparametric Multivariate Profile Monitoring Via Tree Ensembles
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866910527819612160 |
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| author | Timme, Daniel A. Barrientos, Andrés F. Chicken, Eric Sinha, Debajyoti |
| author_facet | Timme, Daniel A. Barrientos, Andrés F. Chicken, Eric Sinha, Debajyoti |
| contents | Monitoring random profiles over time is used to assess whether the system of interest, generating the profiles, is operating under desired conditions at any time-point. In practice, accurate detection of a change-point within a sequence of responses that exhibit a functional relationship with multiple explanatory variables is an important goal for effectively monitoring such profiles. We present a nonparametric method utilizing ensembles of regression trees and random forests to model the functional relationship along with associated Kolmogorov-Smirnov statistic to monitor profile behavior. Through a simulation study considering multiple factors, we demonstrate that our method offers strong performance and competitive detection capability when compared to existing methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_10721 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Nonparametric Multivariate Profile Monitoring Via Tree Ensembles Timme, Daniel A. Barrientos, Andrés F. Chicken, Eric Sinha, Debajyoti Methodology Monitoring random profiles over time is used to assess whether the system of interest, generating the profiles, is operating under desired conditions at any time-point. In practice, accurate detection of a change-point within a sequence of responses that exhibit a functional relationship with multiple explanatory variables is an important goal for effectively monitoring such profiles. We present a nonparametric method utilizing ensembles of regression trees and random forests to model the functional relationship along with associated Kolmogorov-Smirnov statistic to monitor profile behavior. Through a simulation study considering multiple factors, we demonstrate that our method offers strong performance and competitive detection capability when compared to existing methods. |
| title | Nonparametric Multivariate Profile Monitoring Via Tree Ensembles |
| topic | Methodology |
| url | https://arxiv.org/abs/2407.10721 |