Results 211 to 220 of about 248,461 (262)
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A bound on mean square estimate error
International Conference on Acoustics, Speech, and Signal Processing, 1993A lower bound on mean square estimate error is derived as an instance of the covariance inequality by concatenating the generating matrices for the Bhattacharyya and Barankin bounds; it represents a generalization of the Bhattacharyya (1946), Barankin (1949), Cramer-Rao (1945), Hammersley-Chapman-Robbins (1950, 1951), Kiefer (1952), and McAulay ...
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On the mean squared error, the mean absolute error and the like
Communications in Statistics - Theory and Methods, 1999The problem of finding the minimizer of the rth -mean error , is revisited, via a unified approach. The approach is discussed for arbitrary r and is illustrated for r = 1 (mean absolute error)r = 2 (mean squared error), and r = 4. This approach is also discussed in the context of maximum likelihood estimation in a class of symmetric distributions which
Shaul K. Bar-Lev +2 more
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Truncated squarer with minimum mean-square error
Microelectronics Journal, 2014Abstract Squaring is an important arithmetic operation required in a multitude of applications. In this paper we present a truncated squarer that, with an n-bit input, produces its output on a number of bits that can be defined at design time in the [n,2n] range.
PETRA, NICOLA +4 more
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Minimum mean square error vector precoding
European Transactions on Telecommunications, 2006AbstractWe 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.
David A. Schmidt +2 more
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2020
This chapter treats the problem of approximating and estimating the mean squared error of empirical best linear unbiased predictors of small area linear parameters under linear mixed models. This is done in several steps. First, when all the model parameters are unknown. Second, when only the variance component parameters are unknown.
Domingo Morales +3 more
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This chapter treats the problem of approximating and estimating the mean squared error of empirical best linear unbiased predictors of small area linear parameters under linear mixed models. This is done in several steps. First, when all the model parameters are unknown. Second, when only the variance component parameters are unknown.
Domingo Morales +3 more
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Non-mean-square error criteria
IEEE Transactions on Information Theory, 1958While in the engineering literature non-mean-square error criteria for predictors are often presented as physically significant and then shunted aside because of mathematical unmanageability, it is shown here that ia the case of Gaussian processes all such criteria given ia three recent textbooks yield the same predictor as the linear minimum mean ...
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Root mean square error or mean absolute error? Use their ratio as well
Information Sciences, 2022zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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The Synthetic Mean Square Error Control Chart
Communications in Statistics - Simulation and Computation, 2013A synthetic mean square error (MSE) control chart is presented in this study for monitoring the changes in the mean and standard deviation of a normally distributed process. The synthetic MSE control chart is a combination of the standard MSE control chart and the conforming run length (CRL) control chart.
Ming Ha Lee, Michael B. C. Khoo
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On prediction and mean squared error
Canadian Journal of Statistics, 1992AbstractPractical questions motivate the search for predictors either of an as yet unobserved random vector, or of a random function of a parameter. An extension of the classical UMVUE theory is presented to cover such situations. In includes a Rao‐Blackwell‐type theorem, a Cramer‐Rao‐type inequality, and necessary and sufficient conditions for a ...
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An Approximation of FOCUSS Mean Squared Error
2023 20th International Joint Conference on Computer Science and Software Engineering (JCSSE), 2023Bamrung Tausiesakul +1 more
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