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Detecting Critical Change in Dynamics Through Outlier Detection with Time-Varying Parameters.

Meng Chen1, Michael D Hunter2, Sy-Miin Chow2

  • 1Department of Psychology, University of Southern California, Los Angeles, California, USA.

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|September 15, 2025
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Summary
This summary is machine-generated.

This study introduces a novel outlier detection method to identify critical shifts in time-varying parameters (TVPs) within non-stationary intensive longitudinal data. The method effectively detects changes in dynamic functions, aiding in the analysis of complex data structures.

Keywords:
outlier detectionstate‐space modelstime‐varying parameters

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Area of Science:

  • Statistics
  • Psychometrics
  • Data Science

Background:

  • Intensive longitudinal data frequently exhibit non-stationarity, characterized by changing statistical properties over time.
  • Time-varying parameters (TVPs) are often used to model these temporal changes.
  • The dynamics of TVPs can be heterogeneous, influenced by various factors.

Purpose of the Study:

  • To propose a novel outlier detection method for identifying critical shifts in differentiable dynamic functions.
  • To extend this method for detecting changes in the dynamic functions of time-varying parameters (TVPs).
  • To offer a flexible approach applicable to diverse data scenarios.

Main Methods:

  • Developed an outlier detection method for detecting critical shifts in linear and non-linear dynamic functions.
  • The method is designed to be applicable to dynamic functions of time-varying parameters (TVPs).
  • The approach accommodates various data structures: single- and multi-subject, univariate and multivariate, with or without latent variables.

Main Results:

  • Demonstrated the utility and performance of the proposed outlier detection method through three simulation studies.
  • Validated the method's effectiveness on an empirical dataset from a facial electromyography study on emotion induction.
  • The method successfully identified critical shifts in dynamic functions, including those for TVPs.

Conclusions:

  • The proposed outlier detection method provides a robust tool for analyzing non-stationary intensive longitudinal data.
  • It facilitates the detection of critical shifts in time-varying parameters and their underlying dynamic functions.
  • This approach enhances the understanding of complex temporal dynamics in various research contexts.