The Information Criteria of Takeuchi and Akaike

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Related lectures (29)

Regression: Simple and Multiple Linear Log in to Mediaspace to watch this video

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

Linear Regression Basics Log in to Mediaspace to watch this video

Covers the basics of linear regression, including OLS, heteroskedasticity, autocorrelation, instrumental variables, Maximum Likelihood Estimation, time series analysis, and practical advice.

Back to Linear Regression Log in to Mediaspace to watch this video

Covers linear regression, regularization, inverse problems, X-ray tomography, image reconstruction, data inference, and detector intensity.

Overfitting in Supervised Learning: Case Studies and Techniques Log in to Mediaspace to watch this video

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.

Linear Regression: Fundamentals and Applications Log in to Mediaspace to watch this video

Explores linear regression fundamentals, model training, evaluation, and performance metrics, emphasizing the importance of R², MSE, and MAE.

Linear Regression: Model Adjustment and Parameter Estimation Log in to Mediaspace to watch this video

Explains the decomposition of total sum of squares, model adjustment, and parameter estimation in linear regression.

Basics of Linear Regression Log in to Mediaspace to watch this video

Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.

Linear Regression: Ozone Data Analysis Log in to Mediaspace to watch this video

Explores linear regression analysis of ozone data using statistical models.

Supervised Learning: Likelihood Maximization Log in to Mediaspace to watch this video

Covers supervised learning through likelihood maximization to find optimal parameters.

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