Modern Regression: Overdispersion and Model Assessment

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (32)

Page 2 of 4

Covers Generalized Linear Models, likelihood, deviance, link functions, sampling methods, Poisson regression, over-dispersion, and alternative regression models.

Generalised Linear Models: Regression with Exponential Family Responses

Covers regression with exponential family responses using Generalised Linear Models.

Generalized Linear Regression: Classification

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

Linear Regression: Statistical Inference and Regularization

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Regression Methods: Model Building and Inference

Covers Inference, Model Building, Variable Selection, Robustness, Regularised Regression, Mixed Models, and Regression Methods.

Linear and Weighted Regression: Optimal Parameters and Local Solutions

Covers linear and weighted regression, optimal parameters, local solutions, SVR application, and regression techniques' sensitivity.

Nonlinear Machine Learning: k-Nearest Neighbors and Feature Expansion

Covers the transition from linear to nonlinear models, focusing on k-NN and feature expansion techniques.

Generalized Linear Models: Exponential Families and Model Construction

Covers exponential families, model construction, and canonical link functions in Generalized Linear Models.

Regression: Simple and Multiple Linear

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

Probabilistic Models for Linear Regression

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