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AttackResilient Minimum MeanSquared Error Estimation
, 2014
"... Attackresilient minimum meansquared error estimation ..."
Minimum MeanSquare Error
"... All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
Iterative Linear MinimumMeanSquareError Detection
"... Performance analysis of multiary systems with iterative linear minimummeansquareerror detection ..."
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Performance analysis of multiary systems with iterative linear minimummeansquareerror detection
The Estimation Problem of Minimum Mean Squared Error
, 2003
"... Regression analysis of a response variable Y requires careful selection of explanatory variables. The quality of a set of explanatory features X = (X ; :::; X (d) ) can be measured in terms of the minimum mean squared error = min This paper investigates methods for estimating L fr ..."
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Cited by 2 (0 self)
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Regression analysis of a response variable Y requires careful selection of explanatory variables. The quality of a set of explanatory features X = (X ; :::; X (d) ) can be measured in terms of the minimum mean squared error = min This paper investigates methods for estimating L
– Minimum Mean Square Error Estimation
"... • Estimation theory is the most important theory and method in statistical inference • Statistical inference – Data generated in accordance with some unknown probability distribution must be analyzed – Some type of inference about the unknown distribution must be made like the characteristics (param ..."
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(parameters) of the distribution generating the experimental data, the mean and variance etc. ()Φxg {}nXXX,...,, 21=X
Estimator, Minimum Mean Squared Error, Time
"... Recently, statistical process control (SPC) methodologies have been developed to accommodate autocorrelated data. To construct control charts for stationary process data, the process variance needs to be estimated. For an independently identically distributed sequence of a random variable, the varia ..."
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, the variance is usually estimated by the sample variance. For a weakly stationary process, different estimators of the process variance can be used. In this paper, comparisons of estimators of the process variance are made based on the criterion of minimum mean squared error. 1.
Notes on Linear Minimum Mean Square Error Estimators
, 2011
"... Some connections between linear minimum mean square error estimators, maximum output SNR filters and the least square solutions are presented. The notes have been prepared to be distributed with EE 503 (METU, Electrical Engin.) lecture notes. 1 Linear Minimum Mean Square Error Estimators The followi ..."
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Some connections between linear minimum mean square error estimators, maximum output SNR filters and the least square solutions are presented. The notes have been prepared to be distributed with EE 503 (METU, Electrical Engin.) lecture notes. 1 Linear Minimum Mean Square Error Estimators
Minimum Mean Squared Error Interference Alignment
"... Abstract—To achieve the full multiplexing gain of MIMO interference networks at high SNRs, the interference from different transmitters must be aligned in lowerdimensional subspaces at the receivers. Recently a distributed “maxSINR” algorithm for precoder optimization has been proposed that achiev ..."
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Cited by 41 (1 self)
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that achieves interference alignment for sufficiently high SNRs. We show that this algorithm can be interpreted as a variation of an algorithm that minimizes the sum Mean Squared Error (MSE). To maximize sum utility, where the utility depends on rate or SINR, a weighted sum MSE objective is used to compute
Mutual information and minimum meansquare error in Gaussian channels
 IEEE TRANS. INFORM. THEORY
, 2005
"... This paper deals with arbitrarily distributed finitepower input signals observed through an additive Gaussian noise channel. It shows a new formula that connects the inputoutput mutual information and the minimum meansquare error (MMSE) achievable by optimal estimation of the input given the out ..."
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Cited by 285 (32 self)
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This paper deals with arbitrarily distributed finitepower input signals observed through an additive Gaussian noise channel. It shows a new formula that connects the inputoutput mutual information and the minimum meansquare error (MMSE) achievable by optimal estimation of the input given
Functional Properties of Minimum MeanSquare Error and Mutual Information
"... Abstract—In addition to exploring its various regularity properties, we show that the minimum meansquare error (MMSE) is a concave functional of the input–output joint distribution. In the case of additive Gaussian noise, the MMSE is shown to be weakly continuous in the input distribution and Lipsc ..."
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Cited by 9 (1 self)
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Abstract—In addition to exploring its various regularity properties, we show that the minimum meansquare error (MMSE) is a concave functional of the input–output joint distribution. In the case of additive Gaussian noise, the MMSE is shown to be weakly continuous in the input distribution
Results 1  10
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