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Optimal Mean-Squared-Error Batch Sizes
Management Science, 1995When an estimator of the variance of the sample mean is parameterized by batch size, one approach for selecting batch size is to pursue the minimal mean squared error (mse). We show that the convergence rate of the variance of the sample mean, and the bias of estimators of the variance of the sample mean, asymptotically depend on the data process only
Wheyming Tina Song, Bruce W. Schmeiser
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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.
D.A. Schmidt, M. Joham, W. Utschick
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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 ...
Lee, Dominic, Priebe, Carey
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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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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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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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Mean Integrated Squared Error Sampling
Journal of the American Statistical Association, 1986Abstract Stratified sampling is considered, where (a) the mean integrated squared error (MISE) metric is used in place of the mean squared error (MSE) metric; (b) the entire distribution [i.e., f(x)], rather than a property of the distribution [e.g., E(x)], is used as a target of the procedure; (c) the distribution f(x) is estimated by a truncated ...
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