Results 261 to 270 of about 223,012 (309)

The estimation problem of minimum mean squared error

Statistics & Decisions, 2003
Summary: Regression analysis of a response variable \(Y\) requires careful selection of explanatory variables. The quality of a set of explanatory features \(X=(X^{(1)}, \dots, X^{(d)})\) can be measured in terms of the minimum mean squared error \[ L^*= \min_f{\mathbf E} \biggl\{\bigl(Y-f(X) \bigr)^2\biggr\}.
Devroye, Luc, Schäfer, Dominik, Györfi, László, Walk, Harro   +3 more
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Minimum mean-squared error covariance shaping

2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)., 2004
The 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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A Shrinkage Linear Minimum Mean Square Error Estimator

IEEE Signal Processing Letters, 2013
The conventional linear minimum mean square error (LMMSE) estimator is commonly implemented through the sample covariance matrix. This estimator can only be implemented if the sample size N is higher than the observation dimension M. Moreover, this estimator performs poorly when the sample size is not sufficiently large.
Chao-Kai Wen, Jung-Chieh Chen, Pangan Ting   +2 more
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Sparse minimum mean square error(MMSE) blind beamformer

2015 IEEE International Wireless Symposium (IWS 2015), 2015
To reduce implementation cost and power consumption, a sparse minimum mean square error (MMSE) beamformer is designed using the orthogonal matching pursuit algorithm to compute the locations and weights of few selected active antenna elements and estimate the direction-of-arrival (DOA) angles and the number of sources.
null Hua Peng, Naofal Al-Dhahir
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Realizable minimum mean-squared error channel shorteners

IEEE Transactions on Signal Processing, 2005
We 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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Some techniques of minimum mean square error estimation

Microelectronics Reliability, 1988
Abstract A method of minimising the mean square error (MSE) of modified shrunken estimator θ s of the parameter θ of a population is considered. The method used is modified shrinkage of the unbiased estimator θ towards a prior θo in the parameter space.
M.C. Shah, R. Parmar, V.P. Gupta
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Consistency of the minimum mean square error estimate

ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005
The 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. Trussell, M. Civanlar
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Minimum mean-square error stochastic linear control

International Journal of Control, 1968
Optimal control algorithms are developed for discrete and continuous time stochastic linear dynamical systems for an ‘instantaneous ’ weighted mean–square error perfomance measure.The derivations are based on well–known results in matrix analysis and the theory of stochastic linear systems.
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Minimum mean square error nonuniform FIR filter banks

2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221), 2002
A theory for jointly optimizing nonuniform analysis and synthesis FIR filter banks with arbitrary filter lengths and an arbitrary delay through the filter bank is developed. The FIR subband coder is optimized with respect to the minimum mean square error between the output and the input signals under a bit constraint. The subband quantizers are modeled
A. Hjorungnes, T. Saramaki
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Estimation of the minimum mean square error of prediction

Biometrika, 1975
SUMMARY 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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