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High Content Screening Analysis to Evaluate the Toxicological Effects of Harmful and Potentially Harmful Constituents HPHC
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Robust identification of controlled Hawkes processes.

Michael Mark1, Thomas A Weber1

  • 1École Polytechnique Fédérale de Lausanne, Station 5, CH-1015 Lausanne, Switzerland.

Physical Review. E
|May 20, 2020
PubMed
Summary

Estimating Hawkes-like processes is challenging. A new expectation-maximization algorithm improves convergence and provides a lower bound for maximum-likelihood estimates in debt collection analysis.

Area of Science:

  • Statistics
  • Time Series Analysis
  • Computational Statistics

Background:

  • Hawkes-like processes are point processes with self-excitation.
  • Standard estimation methods for these processes often suffer from bias or convergence issues.
  • Accurate identification is crucial for applications like financial modeling.

Purpose of the Study:

  • To develop a robust estimation method for Hawkes-like processes.
  • To improve convergence properties compared to existing techniques.
  • To provide a reliable lower bound for maximum-likelihood estimates.

Main Methods:

  • An expectation-maximization (EM) algorithm is proposed.
  • The algorithm leverages the internal branching structure of the process.

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  • The method is evaluated in the context of consumer debt collection.

    • Main Results:

    • The proposed EM algorithm demonstrates improved convergence behavior.
    • The method yields a tight lower bound for maximum-likelihood estimates.
    • The approach is validated through a practical application in debt collection.

    Conclusions:

    • The novel EM algorithm offers a more reliable approach to estimating Hawkes-like processes.
    • This method enhances the accuracy and stability of parameter estimation.
    • The findings have implications for financial data analysis and risk management.