Nested Model Selection

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Related lectures (31)

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Linear Regression: Estimation and Inference

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

Assessing Significance and Fit

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

Model Checking and Residuals

Explores model checking and residuals in regression analysis, emphasizing the importance of diagnostics for ensuring model validity.

Weighted Least Squares Estimation: IRLS Algorithm

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

Linear Models and Overfitting

Explores linear models, overfitting, and the importance of feature expansion and adding more data to reduce overfitting.

Distribution Theory of Least Squares

Explores the distribution theory of least squares estimators in a Gaussian linear model.

Linear Models: Introduction

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

ANOVA: Partitioning Total SS

Covers ANOVA method, focusing on partitioning total sum of squares into treatment and error components, mean square calculations, Fisher statistic, and F-distribution.

Linear Regression: Estimation and Testing

Explores linear regression estimation, hypothesis testing, and practical applications in statistics.

Linear Regression: Maximum Likelihood Approach

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