Lectures related to Estimating Moments: GEV and GPD

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.

Estimation Methods in Probability and Statistics

Discusses estimation methods in probability and statistics, focusing on maximum likelihood estimation and confidence intervals.

Interval Estimation: Method of Moments

Covers the method of moments for estimating parameters and constructing confidence intervals based on empirical moments matching distribution moments.

Statistical Estimation

Covers statistical estimation methods and explores practical examples of parameter estimation for different distributions.

Confidence Intervals: Definition and Estimation

Explains confidence intervals, parameter estimation methods, and the central limit theorem in statistical inference.

Statistics for Data Science: Introduction to Statistical Methods

Covers the fundamental concepts of statistics and their application in data science.

Bias, Variance, Consistency, EMV

Covers bias, variance, mean squared error, consistency, and maximum likelihood estimation in the Poisson model.

Estimation Methods

Covers various methods for estimating model parameters, such as method of moments and maximum likelihood estimation.

Parameter Estimation

Discusses parameter estimation, including checks, quality, distribution, and statistical properties of estimates.

Probabilistic Models for Linear Regression

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Quantitative Risk Management: Copulas and Generative Adversarial Networks

Explores copulas, simulation algorithms, fitting data with rank correlations, and GANs for image generation.

Statistical Estimation: Properties and Distributions MOOC: Introduction to Discrete Choice Models

Explores statistical parameter estimation, sample accuracy, and Bernoulli variables' properties.

Mixture models: taste heterogeneity MOOC: Selected Topics on Discrete Choice

Explores mixture models in discrete choice and random parameters estimation results.

L-Moment Estimation: Probability-Weighted Moments Log in to Mediaspace to watch this video

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

Maximum Likelihood Theory & Applications Log in to Mediaspace to watch this video

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.

Estimators and Confidence Intervals Log in to Mediaspace to watch this video

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

Estimators and Bias Log in to Mediaspace to watch this video

Explores estimators, bias, and efficiency in statistics, emphasizing the trade-off between bias and variability.

Estimating Parameters: Confidence Intervals Log in to Mediaspace to watch this video

Explores estimating parameters through confidence intervals in linear regression and statistics.

Estimation and Analysis of Deviance Log in to Mediaspace to watch this video

Covers estimation of unknown parameters, analyzing model fit, bird prey response, and model diagnostics.

Estimating Random Variables Log in to Mediaspace to watch this video

Covers the estimation of random variables, including expectations, variances, and quantiles.