Master Copula Theory, Visualization, Estimation, Simulation, and Probability Calculations with the copula Package in R
What you will learn
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Understand the fundamentals of copulas – Learn what copulas are, their mathematical properties, and their role in modeling dependence structures
Explore Sklar’s Theorem – Understand how joint cumulative distribution functions (CDFs) decompose into marginal distributions and a copula function
Learn different types of copulas – Study Gaussian, t-Student, Clayton, and Gumbel copulas and their characteristics
Estimate copula parameters in R – Use the copula package to estimate copula parameters through statistical methods
Perform goodness-of-fit tests – Assess the quality of fitted copula models using statistical criteria such as AIC, BIC, and log-likelihood
Visualize copulas in R – Generate contour plots, 3D surfaces, and scatter plots to interpret dependence structures
Simulate data using copulas – Use copulas to generate synthetic datasets that preserve the dependence structure of modeled data
Analyze dependencies – Compute Kendall’s Tau, Spearman’s Rho, and tail dependence coefficients to measure both typical and extreme event correlations
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