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Minimum Mean Square Error Vector Precoding

, 2004
openaire   +2 more sources

Machine Learning and Shapley Additive Explanations Value Integration for Predicting the Prognostic of Anti-N-Methyl-D-Aspartate Receptor Encephalitis: Model Development and Evaluation Study.

JMIR Med Inform
Wang J, Wu H, Cai H, Wang Y, Gu J.
europepmc   +1 more source

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Mean Squared Error

Encyclopedia of Machine Learning and Data Mining, 2017
R. Grafton, H. Nelson, N. R. Lambie, Paul R. Wyrwoll   +3 more
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Mean Squared Error

Radiopaedia.org, 2008
A. Murphy, Candace Moore
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A review of ridge parameter selection: minimization of the mean squared error vs. mitigation of multicollinearity

Communications in statistics. Simulation and computation, 2022
Ridge Estimation (RE) is a widespread method to overcome the problem of collinearity defining a class of estimators depending on the non-negative scalar parameter k.
C. García-García, Román Salmerón‐Gómez, José García Pérez   +2 more
semanticscholar   +1 more source

Number of Source Signal Estimation by the Mean Squared Eigenvalue Error

IEEE Transactions on Signal Processing, 2018
Detection of the number of source signals (NoSS) in the presence of additive noise is considered. We present a new approach denoted by the mean squared eigenvalue error (MSEE). The MSEE is the mean squared error between the desired noise-free eigenvalues
S. Beheshti, S. Sedghizadeh
semanticscholar   +1 more source

Optimal Mean-Squared-Error Batch Sizes

Management Science, 1995
When an estimator of the variance of the sample mean is parameterized by batch size, one approach for selecting batch size is to pursue the minimal mean squared error (mse). We show that the convergence rate of the variance of the sample mean, and the bias of estimators of the variance of the sample mean, asymptotically depend on the data process only
Wheyming Tina Song, Bruce W. Schmeiser
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Minimum mean square error vector precoding

European Transactions on Telecommunications, 2006
AbstractWe derive theminimum mean square error(MMSE) solution to vector precoding for frequency flat multiuser scenarios with a centralised multi‐antenna transmitter. The receivers employ a modulo operation, giving the transmitter the additional degree of freedom to choose aperturbation vector.
D.A. Schmidt, M. Joham, W. Utschick
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Exact mean and mean squared error of the smoothed bootstrap mean integrated squared error estimator

Computational Statistics, 2000
Let \(X_1, X_2, \ldots, X_n\) be independent and identically distributed with density \(f\), and set \(X=\) \(\{ X_1, \ldots, X_n \}.\) \(\phi\) denotes the standard normal density and for \(\sigma >0\) let \(\phi(x, \sigma^2) = \sigma^{-1}\phi(x\sigma^{-1}).\) The authors consider kernel estimators for \(f\): the Gaussian kernel estimator with ...
Lee, Dominic, Priebe, Carey
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