Concept

Mean squared prediction error

Related lectures (32)

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.

Introduces linear models in machine learning, covering basics, parametric models, multi-output regression, and evaluation metrics.

Covers Monte Carlo methods, variance reduction, and stochastic optimal control, exploring simulation techniques, efficiency, and investment dynamics.

Explores spectral estimation for Gaussian and binary signals in the spiked matrix estimation problem, analyzing the impact of signal-to-noise ratio.

Explores thresholding, ROC curves, regression evaluation, naive methods, and model selection in machine learning.

Explains estimation through mean-squared error and Fisher information in the context of adaptive filters and exponentiated distributions.

Covers the fundamentals of detection and estimation theory, focusing on mean-squared error and hypothesis testing.

Explores model selection in least squares regression, addressing multicollinearity challenges and introducing shrinkage techniques.

Explores the Method of Moments, Bias-Variance tradeoff, Consistency, Plug-In Principle, and Likelihood Principle in point estimation.

Explains maximum likelihood estimation, MSE, Fisher information, and Cramér-Rao bound in statistical inference.

Explains statistical estimators for random variables and Gaussian distributions, focusing on error functions for integration.