Parametric Models: Logistic and Dirichlet Densities

Related lectures (32)

Page 3 of 4

Explores dependence concepts, copulas, correlation fallacies, and rank correlations in statistics.

Covers Extreme Value Theory, GEV distributions, GPD, and threshold exceedances.

Explores nonparametric statistics, covering estimation methods and the bias-variance tradeoff.

Explores non-parametric statistics, Bayesian methods, and linear regression with a focus on kernel density estimation and posterior distribution.

Covers L-moment estimation, probability-weighted moments, and maximum likelihood inference basics.

Introduces the basics of probability models, covering random variables, distributions, and statistical estimation.

Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.

Explores bivariate data analysis, correlation, and regression techniques for model assessment.

Explores completeness, minimal sufficiency, and special statistical models, focusing on exponential and transformation families.

Covers copulas, Sklar's Theorem, types of copulas, and simulation of copulas for risk management.