Estimation and Analysis of Deviance

Related lectures (32)

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

Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.

Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.

Explores the Method of Moments, Bias-Variance tradeoff, Consistency, Plug-In Principle, and Likelihood Principle in point estimation.

Explores statistical models, parameter estimation, and sampling distributions in probability and statistics.

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

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

Explores logistic regression for binary response variables, covering topics such as odds ratio interpretation and model fitting.

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

Introduces Fisher Information for parameter estimation based on collected data.