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Consistency of the minimum mean square error estimate
ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005The minimum mean square error estimate for the deconvolution problem of a Gaussian signal in Gaussian noise is shown to be feasible in the sense of being inside closed convex sets defined by the noise statistics. It is pointed out that there is some a priori knowledge which is not satisfied by the Wiener solution but the set formed by this information ...
H. Joel Trussell, M. Reha Civanlar
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Minimum mean-square error quadrature
Journal of Statistical Computation and Simulation, 1993Minimum mean squared error linear estimators of the area under a curve are considered for cases when the observations are observed with error. The underlying functional form giving rise to the observations is left unspecified, leading to use of quadrature estimators for the true area.
Walter W. Piegorsch, A. John Bailer
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Functional Properties of Minimum Mean-Square Error and Mutual Information
IEEE Transactions on Information Theory, 2012In addition to exploring its various regularity properties, we show that the minimum mean-square 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 Lipschitz continuous with respect to the quadratic Wasserstein ...
Yihong Wu, Sérgio Verdú
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Estimation of the minimum mean square error of prediction
Biometrika, 1975SUMMARY Bloomfield (1973) and Jones (1964) have discussed the estimation of the error of prediction of a time series. Their results use the asymptotic normality of their estimates and we attempt to examine the validity of this approximation in the simplest case.
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Minimum mean-squared error covariance shaping
2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)., 2004The paper develops and explores applications of a linear shaping transformation that minimizes the mean squared error (MSE) between the original and shaped data, i.e., that results in an output vector with the desired covariance that is as close as possible to the input, in an MSE sense.
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On minimum mean square error speech enhancement
[Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing, 1991Motivation for using minimum mean square error (MMSE) estimation in noisy speech enhancement problems is given. An MMSE estimator which is based on hidden Markov modeling of the clean signal as well as the noise process is systematically developed. The MMSE estimator is tested and compared with the spectral subtraction estimator in vector quantization ...
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Fixed-width CSD multipliers with minimum mean square error
Proceedings of 2010 IEEE International Symposium on Circuits and Systems, 2010Many multimedia and DSP applications require fixed-width multipliers, in which input data and output results have the same bit width. In this paper we investigate fixed-width multipliers where one of the input operand is a constant, encoded using canonic signed digit (CSD) representation.
Nicola Petra +6 more
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Source imaging with minimum mean-squared error
The Journal of the Acoustical Society of America, 1993Prior knowledge of source size, shape, and radiation process, and of receiver noise correlations are incorporated in a linear minimum mean-squared error (MMSE) imaging estimator. Its resolution operator and expected squared estimation error are derived and are computed for discrete linear source and receive arrays.
Roland Stoughton, Stewart Strait
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Moments and error expressions in polynomial minimum mean square estimatior
Information Sciences, 1976The mathematical complexity of the minimum mean square estimators made inevitable the consideration of suboptimal solutions, such as the linear minimum mean square (m.m.s.) estimators. The compromise between performance and complexity can be, in general, less serious if the estimator that will substitute the optimum one is polynomial.
Demetrios Kazakos, P. Papantoni-Kazakos
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Realizable minimum mean-squared error channel shorteners
IEEE Transactions on Signal Processing, 2005We present an analysis of realizable (i.e., causal, stable, and of finite degree) minimum mean-squared error (MMSE) channel shorteners for multiple-input multiple-output (MIMO) systems, driven by spatially and temporally white signals, and subject to a constant output power constraint.
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