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Real-time changepoint detection in a nonlinear expectile model.

Gabriela Ciuperca1, Matúš Maciak2, Michal Pešta2

  • 1Institut Camille Jordan, Université Lyon 1, 43 blvd du 11 Novembre 1918, Lyon, 69622 France.

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|June 26, 2023
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Summary
This summary is machine-generated.

This study introduces a novel online changepoint detection method using conditional expectiles, enhancing data analysis robustness and model flexibility for real-time applications.

Keywords:
Asymmetric least squaresChangepoint testCoherent risk measureConditional expectilesOnline detection

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Area of Science:

  • Statistics
  • Econometrics
  • Data Science

Background:

  • Changepoint detection is crucial for identifying shifts in data.
  • Existing methods may lack robustness to asymmetric error distributions.
  • Conditional expectiles offer a coherent and elicitable risk measure.

Purpose of the Study:

  • Introduce a new online changepoint detection procedure.
  • Enhance flexibility and interpretability of changepoint models.
  • Provide a robust statistical test for detecting changes in data streams.

Main Methods:

  • Utilizing conditional expectiles for changepoint detection.
  • Employing a nonlinear underlying model with a parametric regression function.
  • Developing a consistent statistical test with distribution independent of model parameters.

Main Results:

  • The proposed method demonstrates improved robustness, particularly with asymmetric errors.
  • The statistical test is proven consistent and parameter-independent.
  • Empirical evaluation via simulation and real-world COVID-19 data confirms practical applicability.

Conclusions:

  • The new procedure offers a flexible and robust approach to online changepoint detection.
  • Conditional expectiles provide significant advantages in handling diverse data distributions.
  • The method is suitable for real-time analysis and practical applications in various fields.