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Concept

Generalized linear model

Related lectures (32)

Generalised Linear Models: Regression with Exponential Family Responses

Covers regression with exponential family responses using Generalised Linear Models.

Regression Diagnostics

Covers regression diagnostics for linear models, emphasizing the importance of checking assumptions and identifying outliers and influential observations.

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.

Linear Models: Introduction

Introduces linear models, regression, Gaussian distribution, linearity, and model generalization.

Generalized Linear Models: Theory and Applications

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

Generalized Linear Models: Examples and Applications

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

Assessing Significance and Fit

Covers confidence intervals, R2, and examples on cement heat evolution and car horsepower-MPG relationships.

Generalized Linear Regression: Classification

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

Weighted Least Squares Estimation: IRLS Algorithm

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

Multicollinearity: Dangers and Remedies

Explores the dangers of multicollinearity in linear models and discusses diagnostic methods and remedies.

Provable Benefits of Overparameterization in Model Compression

Explores the provable benefits of overparameterization in model compression, emphasizing the efficiency of deep neural networks and the importance of retraining for improved performance.

Categorical Data Analysis: Poisson GLM

Explores Poisson GLM for analyzing count data in categorical analysis, focusing on contingency tables and Bayesian inference.

Ridge Regression: Penalised Least Squares

Explores Ridge Regression for handling multicollinearity and the LASSO method for model selection.

Generalized Linear Regression

Explores generalized linear regression, logistic regression, and multiclass classification in machine learning.

Linear Models for Classification

Explores linear models for classification, logistic regression, and gradient descent in machine learning.

Linear Models: Least Squares

Explores linear models, least squares, Gaussian vectors, and model selection methods.

Nested Model Selection

Explores nested model selection in linear models, comparing models through sums of squares and ANOVA, with practical examples.

Algebraic statistics: tables and networks

Explores algebraic statistics, log-linear models, and network testing for model goodness of fit.

Logistic Regression: Modeling Binary Response Variables

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

Parameter estimation MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

Explores parameter estimation in neuron models, focusing on quadratic optimization and linear fit.