Mean squared prediction error

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

Delves into model evaluation, covering theory, training error, prediction error, resampling methods, and information criteria.

Explores probabilistic estimation in the Spin Glass Card game, emphasizing room splitting based on a given parameter.

Explores linear estimation, optimal criteria, and the orthogonality principle for good choices in estimation.

Explores linear estimation and prediction in AR parametric models, focusing on Yule Walker equations and Wiener filter.

Explores the mathematical reasoning behind the consistency of maximum likelihood estimation.

Covers structural modeling, state space models, and the Kalman filter in time series analysis.

Explores statistical analysis, mean square error, and bootstrapping methods in optimization and simulation.

Explains models with interactions, emphasizing the significance of factors and F-values.

Explores the Spike Wigner model, Bayesian denoising, state evolution, and spectral methods in matrix analysis.