Dynamic Item Response Modeling and Its Application in Health Economics
International Statistical Institute (ISI)
📌About the Event
Dynamic Item Response Modeling and Its Application in Health Economics is an upcoming online webinar by the International Statistical Institute (ISI). The session will focus on how Dynamic Item Response Models can be used for longitudinal health assessment, especially to understand health changes among older populations. The webinar will be led by Prof. Kiranmoy Das and will cover Bayesian modeling, MCMC estimation, shrinkage priors, validation methods, and applications in health economics, health sciences, psychometrics, and economics.
ℹ️ Event Details
- Event Type: Webinar / Academic Session
- Mode of Conduct: Online
- Speaker: Prof. Kiranmoy Das
- Relevant For: Students, researchers, statisticians, data science learners, health economics enthusiasts, and professionals interested in Bayesian modeling and health analytics
- Key Topics Covered:
- Dynamic Item Response Models for longitudinal health assessment
- Bayesian hierarchical frameworks and MCMC estimation
- Lasso-type shrinkage priors for variable selection
- Posterior predictive checks for model validation
- Applications in health economics, health sciences, psychometrics, and economics.
📅 Important Dates
- Webinar Date: 10 June 2026
- Time: 15:30 CEST
- India Time: 7:00 PM IST
🎯 Eligibility & Rules
- Eligibility:
- Open to learners and professionals interested in statistics, health economics, biostatistics, data science, psychometrics, and research methods
- Suitable for students pursuing statistics, economics, public health, data science, mathematics, or related fields
- Prior knowledge of Bayesian statistics or item response theory may help, but no formal eligibility criteria were publicly mentioned.
🏆 Learning Incentives
- Gain exposure to advanced statistical modeling in health economics.
- Learn how dynamic item response models can help study complex health patterns over time.
- Useful for students and researchers interested in health data analytics, Bayesian modeling, and applied statistics.