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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.
The British Journal of Mathematical and Statistical Psychology
|September 15, 2025
Summary
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.
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.
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