Results 1 to 10 of about 2,377,021 (287)

Does Past Myopia Progression Predict Future Progression? [PDF]

Invest Ophthalmol Vis Sci
Beaulieu WT, Repka MX, Pineles SL, Chandler DL, Pang Y, Kehler LAF, Jordan CO, Lee KA, Petersen DB, Hatt SR, Cotter SA, Kraker RT, Weise KK, Holmes JM, Pediatric Eye Disease Investigator Group.   +14 more
europepmc   +1 more source

Are we there yet? Estimating the waves of follow-up required for stable effect estimates in cognitive aging research. [PDF]

Am J Epidemiol
Thoma MC, Wang J, Mayeda ER, McCulloch CE, Hayes-Larson E, Torres JM, Glymour MM.   +6 more
europepmc   +1 more source

Minimum Mean Square Error Vector Precoding

, 2004
openaire   +2 more sources

Optimal Mean-Squared-Error Batch Sizes

Management Science, 1995
When 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
openaire   +2 more sources

Minimum mean square error vector precoding

European Transactions on Telecommunications, 2006
AbstractWe 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
openaire   +1 more source

Exact mean and mean squared error of the smoothed bootstrap mean integrated squared error estimator

Computational Statistics, 2000
Let \(X_1, X_2, \ldots, X_n\) 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
openaire   +2 more sources

Mean Squared Error of EBLUPs

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, María Dolores Esteban, Agustín Pérez, Tomáš Hobza   +3 more
openaire   +1 more source

Minimum mean-square error quadrature

Journal of Statistical Computation and Simulation, 1993
Minimum 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
openaire   +1 more source