2022 Webinar - Higher Learning: An Introduction to Hierarchical Linear Models - February 24
Generalized linear models have been mainstream for quite a few years. However, the reflection of credibility in such models is not necessarily a settled issue. Hierarchical models (also known as fixed and random effects models, or linear mixed models) are an established technique to address this. Under certain conditions, they will generate parameter estimates that are identical to the Bühlmann-Straub credibility estimates that have been part of actuarial practice for decades.
Attendees should be familiar with scripting languages like R, Python, Julia or similar.
Learning Objectives:
- What is a hierarchical model and how does it differ from a standard GLM?
- What assumptions are relevant for hierarchical models and how does one test them?
- How can a hierarchical model be implemented in a traditional actuarial estimation context?
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