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Asymptotic Classification Error for Heavy-Tailed Renewal Processes

arXiv 2408.10502 ↗First posted 20 Aug 2024stat.ML cs.LG math.STcited by 0

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Abstract (arXiv)

Despite the widespread occurrence of classification problems and the increasing collection of point process data across many disciplines, study of error probability for point process classification only emerged very recently. Here, we consider classification of renewal processes. We obtain asymptotic expressions for the Bhattacharyya bound on misclassification error probabilities for heavy-tailed renewal processes.

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References · 4

  1. 2018Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approachcited by 4
  2. 2022Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Modelscited by 3
  3. 2023Bayes Risk Consistency of Nonparametric Classification Rules for Spike Trains Datacited by 1
  4. 2024Asymptotic Error Rates for Point Process Classificationcited by 1

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