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BAYESIAN ANALYSIS OF REPEATED EVENTS USING EVENT-DEPENDENT FRAILTY MODELS: AN APPLICATION TO BEHAVIORAL OBSERVATION
Getachew A Dagne1, James Snyder
1University of South Florida, 13201 Bruce B. Downs, MDC 56, Tampa, FL 33612.
Communications in Statistics: Theory and Methods
|February 18, 2010
Summary
This study introduces new statistical models for analyzing repeated behaviors in social interactions. These event-dependent random effects models better capture the sequence of behaviors within families, improving data analysis.
Area of Science:
- Social Sciences
- Statistics
- Psychology
Background:
- Analyzing repeated behaviors in social interactions is crucial.
- Existing statistical models (Weibull, Cox proportional hazards) with random effects (frailty) handle correlated behaviors within dyads but not event ordering.
- The sequence of behaviors can influence subsequent behavior likelihood.
Purpose of the Study:
- To develop novel event-dependent random effects models for analyzing repeated behaviors data.
- To address the limitation of existing models in accounting for the order of event occurrences within dyads.
- To apply these models to understand emotion regulation in families with children exhibiting behavioral or emotional problems.
Main Methods:
- Development of event-dependent random effects models.
- Utilizing a Bayesian approach for statistical inference.
- Application and illustration using a dataset on family emotion regulation.
Main Results:
- The proposed models effectively account for the sequential nature of behaviors.
- Event-dependent effects provide a more nuanced understanding of behavioral patterns.
- Demonstrated utility in analyzing complex family interaction data.
Conclusions:
- Event-dependent random effects models offer an advancement for analyzing repeated behaviors data.
- These models provide a more accurate representation of behavioral dynamics in social interactions.
- The approach is valuable for research on family dynamics and child behavioral issues.
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