Model Selection

Related lectures (32)

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Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.

Explores model selection methods in biostatistics, emphasizing the importance of starting with a sensible model.

Covers model complexity, overfitting, and strategies to select appropriate machine learning models.

Explores specification testing, machine learning, overfitting, regularization, prediction tests, and variable selection.

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

Covers probability, random variables, expectation, GLMs, hypothesis testing, and Bayesian statistics with practical examples.

Explores regression analysis tools, model selection, influential points, outliers, and diagnostic plots.

Explores the General Linear Model, significance testing, model selection, and parameter inference.

Explores statistical inference for linear models, covering model fitting, parameter estimation, and variance decomposition.