Generalized linear model

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Related lectures (32)

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

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

Chimpanzee Data Analysis

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

Regularization in Machine Learning

Introduces regularization techniques to prevent overfitting in machine learning models.

Optimality and Asymptotics

Explores the optimality of the Least Squares Estimator and its large sample distribution.

Estimation, Shrinkage and Penalization

Covers estimation, shrinkage, and penalization in statistics for data science, emphasizing the importance of balancing bias and variance in model estimation.

Regression: Linear Models

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

Likelihood Estimation and Least Squares

Introduces simple and multiple normal linear regression, and maximum likelihood estimation with practical examples.

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