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Evaluating Discrete Time Methods for Subgrouping Continuous Processes
Jonathan J Park1, Zachary F Fisher1, Sy-Miin Chow1
1Department of Human Development and Family Studies, The Pennsylvania State University.
Multivariate Behavioral Research
|August 17, 2023
Summary
Discrete-time subgrouping methods, like vector autoregression (VAR), effectively identify human process dynamics when data measurement intervals capture system behavior. This research clarifies their utility for continuous-time data analysis.
Area of Science:
- Psychometrics
- Computational Social Science
- Time Series Analysis
Background:
- Human process modeling increasingly focuses on time scales and heterogeneity.
- Discrete-time subgrouping methods, such as vector autoregression (VAR), are used to find shared trends in individual data.
- The accuracy of VAR parameters depends on data measurement intervals.
Purpose of the Study:
- To evaluate the strengths and limitations of discrete-time subgrouping methods in recovering subgroup dynamics under varying measurement intervals.
- To clarify the implications of using discrete-time methods (scgVAR, S-GIMME) on continuous-time data.
Main Methods:
- Monte Carlo simulation study.
- Application of discrete-time subgrouping methods (subgrouped chain graphical VAR, S-GIMME) to continuous-time data.
- Analysis of subgroup recovery under different measurement intervals.
Main Results:
- Discrete-time subgrouping methods successfully recover true subgroups when measurement intervals are sufficiently large.
- Adequate intervals capture the system's dynamics through lagged or contemporaneous effects.
- Performance is contingent on the relationship between measurement interval and system dynamics.
Conclusions:
- Discrete-time subgrouping methods can be reliable for analyzing continuous-time data if measurement intervals are appropriately chosen.
- Understanding the interplay between measurement intervals and system dynamics is crucial for accurate subgroup identification.
- Further research is needed to explore the limitations and implications in diverse modeling contexts.
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