The article compares two methods for estimating copula parameters: maximum-likelihood (ML) and minimum-distance (MD) estimators. The researchers used simulations to test these methods on different types of copulas. They found that ML estimators have smaller biases and are more efficient than MD estimators, especially for archimedean copulas. The bias and efficiency of MD estimators depend heavily on the parameter's location, while ML estimators are more stable across the parameter range.