Lectures related to Inference: Model Checking

Weighted Least Squares Estimation: IRLS Algorithm

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

Generalized Linear Regression: Classification

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

Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.

Generalized Linear Models: Examples and Applications

Explores special examples of Generalized Linear Models, covering logistic regression, count data models, separation issues, and nonparametric relationships.

Logistic Regression: Modeling Binary Response Variables

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

Generalized Linear Models: Theory and Applications

Covers the theory and applications of Generalized Linear Models, including MLE, measures of fit, shrinkage, and special examples.

Regression and Classification

Explores regression, classification, linear models, decision trees, and random forests in data analysis.

Generalized Linear Models: GLMs for Non-Gaussian Data

Explores Generalized Linear Models for non-Gaussian data, covering interpretation of natural link function, MLE asymptotic normality, deviance measures, residuals, and logistic regression.

Parametric Models

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

Modern Regression: Spring Barley Data Log in to Mediaspace to watch this video

Covers inference, weighted least squares, spring barley data analysis, and smoothing techniques.

Modern Regression: Overdispersion and Model Assessment Log in to Mediaspace to watch this video

Explores overdispersion, model assessment, and regression techniques for count data.

Modern Regression: Spring Barley Data Log in to Mediaspace to watch this video

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

Generalized Linear Models: A Brief Review Log in to Mediaspace to watch this video

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

Marginal Models: Interpretation and Application Log in to Mediaspace to watch this video

Explores marginal models in modern regression, emphasizing interpretation and application in statistical analysis.

Generalized Linear Models Log in to Mediaspace to watch this video

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

Modern Regression: Statistical Models and Data Analysis Log in to Mediaspace to watch this video

Introduces regression analysis, covering linear and nonlinear models, Poisson regression, and failure time analysis using various datasets.

Modern Regression: Inference and Models Log in to Mediaspace to watch this video

Covers iterative weighted least squares, model checking, and generalized linear models in regression analysis.

Inference: Poisson Regression Log in to Mediaspace to watch this video

Covers iterative weighted least squares, model checking, Poisson regression, and fitting multinomial models using Poisson errors.

Chimpanzee Data Analysis Log in to Mediaspace to watch this video

Covers the analysis of chimpanzee data on learning times and explores generalized linear models and logistic regression.

Supervised Learning Essentials Log in to Mediaspace to watch this video

Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.