Estimation and Analysis of Deviance

Related lectures (32)

Page 3 of 4

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

Explores confidence intervals, Gaussian estimation, Cramér-Rao inequality, and Maximum Likelihood Estimators.

Explores parameter estimation, standard errors, and confidence intervals using the central limit theorem and practical examples.

Covers Conditional Maximum Likelihood estimation, contribution to likelihood, and MEV model application in choice-based samples.

Covers point estimators for statistical parameter estimation, including methods and calculations.

Explores the IRLS algorithm for weighted least squares estimation in GLM.

Explores statistical estimation, regression models, and model selection in parametric models.

Covers the linear mixed model, including fixed and random effects, estimation, and inference techniques.

Explores the panel effect model with fixed and random effects, discussing estimation challenges and the impact of serial correlation.

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