Results 261 to 270 of about 1,354,279 (305)

A bound on mean square estimate error

International Conference on Acoustics, Speech, and Signal Processing, 1993
A 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 ...
openaire   +3 more sources

The estimation of the mean squared error of small-area estimators

, 1990
Small-area estimation has received considerable attention in recent years because of a growing demand for reliable small-area statistics. The direct-survey estimators, based only on the data from a given small area (or small domain), are likely to yield ...
N. Prasad, J. Rao
semanticscholar   +1 more source

A statistical test for the mean squared error

Journal of Statistical Computation and Simulation, 1999
The likelihood ratio test for a specified value of the mean squared error is derived assuming observation from a normal distribution. The mean squared error is a well established tool for assessing closeness to a target value when bias as well as sampling error (or measurement error) is taken into account.
Erik Holst, Poul Thyregod
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Mean-Squared-Error Methods for Selecting Optimal Parameter Subsets for Estimation

, 2012
Engineers who develop fundamental models for chemical processes are often unable to estimate all of the parameters, especially when available data are limited or noisy.
Kevin A. P. McLean, Shaohua Wu, K. McAuley   +2 more
semanticscholar   +1 more source

Mean Square Error Estimation in Thresholding

IEEE Signal Processing Letters, 2011
We 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.
Tom Chau, Masoud Reza Hashemi, Ervin Sejdic, Soosan Beheshti   +3 more
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Mean Integrated Squared Error Sampling

Journal of the American Statistical Association, 1986
Abstract 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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SELECTION OF SIMPLIFIED MODELS: II. DEVELOPMENT OF A MODEL SELECTION CRITERION BASED ON MEAN SQUARED ERROR

, 2011
Simplified models (SMs) with a reduced set of parameters are used in many practical situations, especially when the available data for parameter estimation are limited.
Shaohua Wu, K. McAuley, T. Harris
semanticscholar   +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.
A. John Bailer, Walter W. Piegorsch
openaire   +2 more sources

Mean Squared Error of Yield Prediction by SOYGRO

Agronomy Journal, 1995
AbstractYield prediction is often one of the major intended uses of a crop simulation model. It is therefore important to evaluate how well a model performs as a predictor. The purpose of this study was to evaluate and analyze how well the SOYGRO model predicts soybean yield, using as a criterion the mean squared error of prediction (MSEP).
Colson, J., Wallach, Daniel, Bouniols, A., Denis, J.B., Jones, J.W.   +4 more
openaire   +3 more sources

Minimum Mean-Squared-Error Quantization in Speech PCM and DPCM Systems

IEEE Transactions on Communications, 1972
The optimum (minimum mean-squared-error) and optimum uniform quantizing characteristics for Laplacian- and gamma-distributed signals are given in tabular form.
M. Paez, T. Glisson
semanticscholar   +1 more source