Root-mean-square deviation

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

Explores regression model performance, learning errors, and building regression trees using the CART algorithm.

Explores statistical dispersion and its impact on determining normal values and data analysis.

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

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

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

Covers adaptive signal processing and the LMS filter for iterative minimization and white input signals.

Introduces Fisher Information for parameter estimation based on collected data.

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

Explores the convergence analysis of stochastic gradient algorithms under various operational modes and step-size sequences.