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Exact mean and mean squared error of the smoothed bootstrap mean integrated squared error estimator
Computational Statistics, 2000Let \(X_1, X_2, \ldots, X_n\) 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 ...
Dominic Lee, Carey E. Priebe
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Mean Square Error Estimation in Thresholding
IEEE Signal Processing Letters, 2011We present a novel approach to estimating the mean square error (MSE) associated with any given threshold level in both hard and soft thresholding. The estimate is provided by using only the data that is being thresholded. This adaptive approach provides probabilistic confidence bounds on the MSE.
Soosan Beheshti +3 more
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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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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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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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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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