Copulas and Extremes

Related lectures (32)

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Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Explores parametric models in data analysis, covering regression estimators, optimization problems, and statistical models.

Explores linear models for classification, logistic regression, and gradient descent in machine learning.

Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.

Explores logistic regression for predicting vegetation proportions in the Amazon region through remote sensing data analysis.

Explores asymmetric parametric models and the extremal coefficient in extreme value dependence.

Explores bivariate maxima, parametric models, distributions, and dependence functions in statistics.

Explores maximum likelihood estimation, logistic regression, covariance estimation, and support vector machines for classification problems.

Explores mixture models, including discrete and continuous mixtures, and their application in capturing taste heterogeneity in populations.

Covers Canonical Correlation Analysis, a method to find relationships between two sets of variables.