Academic Journal
Adaptive Mode Learning for Nonstationary Streaming Data.
| Title: | Adaptive Mode Learning for Nonstationary Streaming Data. |
|---|---|
| Authors: | Wang, Tao1 (AUTHOR) taow@uvic.ca |
| Source: | Journal of Computational & Graphical Statistics. Jul2026, p1-39. 39p. 11 Illustrations. |
| Subject Terms: | *Estimation theory, *Forecasting, Probability density function, Machine learning, Statistical learning |
| Abstract: | AbstractWe develop a novel framework for adaptive conditional mode estimation in streaming environments with evolving, nonstationary data generating processes. In contrast to traditional mean or quantile estimators, which can be suboptimal under skewed or multimodal distributions, our approach targets the conditional mode to provide a robust and interpretable summary of the most likely outcome given covariates. The proposed estimator relies on a recursive kernel update of the joint density using exponentially weighted observations, enabling real-time computation with bounded memory. To accommodate temporal distributional drift, we introduce a prediction-discrepancy-driven bandwidth adaptation mechanism and a cross-validated forgetting strategy that tune parameters online. We develop theoretical results under both stationary and nonstationary settings, including pointwise and uniform consistency results, non-asymptotic error bounds, and minimax-optimal convergence rate over Hölder classes. From an online learning perspective, we derive a sublinear cumulative regret bound and confirm the estimator’s optimality through a matching minimax lower bound. The method is further shown to be robust to heavy-tailed noise and locally adaptive to unknown smoothness. Simulation studies and an empirical application to bike-sharing demand data demonstrate the estimator’s practical adaptability and predictive accuracy. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Computational & Graphical Statistics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Items | – Name: Title Label: Title Group: Ti Data: Adaptive Mode Learning for Nonstationary Streaming Data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Tao%22">Wang, Tao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> taow@uvic.ca</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Computational+%26+Graphical+Statistics%22">Journal of Computational & Graphical Statistics</searchLink>. Jul2026, p1-39. 39p. 11 Illustrations. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Estimation+theory%22">Estimation theory</searchLink><br />*<searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Probability+density+function%22">Probability density function</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+learning%22">Statistical learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: AbstractWe develop a novel framework for adaptive conditional mode estimation in streaming environments with evolving, nonstationary data generating processes. In contrast to traditional mean or quantile estimators, which can be suboptimal under skewed or multimodal distributions, our approach targets the conditional mode to provide a robust and interpretable summary of the most likely outcome given covariates. The proposed estimator relies on a recursive kernel update of the joint density using exponentially weighted observations, enabling real-time computation with bounded memory. To accommodate temporal distributional drift, we introduce a prediction-discrepancy-driven bandwidth adaptation mechanism and a cross-validated forgetting strategy that tune parameters online. We develop theoretical results under both stationary and nonstationary settings, including pointwise and uniform consistency results, non-asymptotic error bounds, and minimax-optimal convergence rate over Hölder classes. From an online learning perspective, we derive a sublinear cumulative regret bound and confirm the estimator’s optimality through a matching minimax lower bound. The method is further shown to be robust to heavy-tailed noise and locally adaptive to unknown smoothness. Simulation studies and an empirical application to bike-sharing demand data demonstrate the estimator’s practical adaptability and predictive accuracy. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Computational & Graphical Statistics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/10618600.2026.2708056 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 39 StartPage: 1 Subjects: – SubjectFull: Estimation theory Type: general – SubjectFull: Forecasting Type: general – SubjectFull: Probability density function Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Statistical learning Type: general Titles: – TitleFull: Adaptive Mode Learning for Nonstationary Streaming Data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Tao IsPartOfRelationships: – BibEntity: Dates: – D: 21 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 10618600 Titles: – TitleFull: Journal of Computational & Graphical Statistics Type: main |
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