Lesson 5 Beyond Metrics
Why traditional parametric multivariate distributions fall short — and the key insight that motivates copulas: separating marginal distributions from dependence structure.
We examine how outliers affect sample statistics, explore kernel density estimation (KDE) as an alternative to parametric assumptions, and build intuition for why the bivariate Gaussian's single dependence parameter (ρ) is insufficient for real-world data.
Part 5 of 14 in the Copula Short Course.
Follow along with the Jupyter notebook:
https://github.com/kkarrancsu/copula-...
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📓 Get the full course materials: https://kirantrillium.gumroad.com/l/c...
Includes 14 interactive Jupyter notebooks, 4 real-world case studies, 17 exercises with solutions, and code templates.