Dependence of the

Mean-Square Error (MSE) as a function of epoch for case 1.

When estimating LASSO models for large cross-sectional data, the penalty rate is selected by retaining multiple hold-out samples and selecting the LASSO penalty rate that produces the smallest average

mean-square error across the hold-out samples.

For image perceptibility, popular evaluation criteria are based on

mean-square error (MSE), Euclidean distance (ED), peak- signal-to-noise ratio (PSNR) and normalized correction (NC) [1, 2].

Common expression for estimation of minimal

mean-square error of informative attributes selection is written down as:

He covers matched filtering, zero-forcing decision feedback equalization, linear equalization, minimum

mean-square error and maximum likelihood decision feedback equalization, maximum likelihood sequence detection, advanced topics, and practical considerations.

When designing an adaptive algorithm, one faces a trade-off between the initial convergence speed and the

mean-square error in steady state.

To model error after averaging of small number of samples N the

mean-square error (MSE) was chosen as suitable parameter of dithering and averaging performance rating for finding an optimal noise dispersion.

RMSE: root

mean-square error, MBE: mean bias error, MABE: mean absolute bias error, MPE: mean percentage error, and MAPE: mean absolute percentage error.