Likelihood Inference

Related lectures (30)

Page 2 of 3

Covers iterative weighted least squares, Poisson regression, and Bayesian analysis of spring barley data using mixed models.

Covers simple and multiple linear regression, including least squares estimation and model diagnostics.

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

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, including OLS estimators, hypothesis testing, and confidence intervals.

Covers regression with exponential family responses using Generalised Linear Models.

Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.

Covers the IWLS algorithm for obtaining MLEs in regression models and discusses the likelihood ratio statistic and deviance in model fitting.

Covers linear regression, regularization, and probabilistic models in generating labels.

Covers linear regression topics including confidence intervals, variance, and maximum likelihood approach.