Results 241 to 250 of about 62,351 (294)

On Multilevel Best Linear Unbiased Estimators

SIAM/ASA Journal on Uncertainty Quantification, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Daniel Schaden, Elisabeth Ullmann
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Constrained Best Linear and Widely Linear Unbiased Estimation

2018 52nd Asilomar Conference on Signals, Systems, and Computers, 2018
The least squares estimator (LSE) and the best linear unbiased estimator (BLUE) are two well-studied approaches for the estimation of deterministic but unknown parameters. In situations where the parameter vector is subject to linear constraints, the constrained LSE can be employed. In this paper, we derive the constrained version of the BLUE. In fact,
Oliver Lang, Alexander Onic, Markus Steindl, Mario Huemer   +3 more
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Best Linear Unbiased Estimation for Multivariate Stationary Processes

Technometrics, 1968
The general linear hypothesis is formulated for a multivariate stationary stochastic process. The best (minimum variance) linear unbiased estimates are derived for the regression functions and it is shown that many signal estimation problems are special cases of the general linear model.
Robert H. Shumway, William C. Dean
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Best Linear Unbiased Estimators for Stereology

Biometrics, 1980
Precise criteria have been published recently for obtaining unbiased ratio estimators of structural parameters, defined in an n-dimensional opaque specimen, from observations in lower-dimensional sections. In this paper, the possibility is shown of obtaining linear unbiased estimators of minimum variance whenever the data can be described by a linear ...
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Some efficiency properties of best linear unbiased estimators

Journal of Statistical Planning and Inference, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Balakrishnan, N., Rao, C. R.
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Best linear unbiased quantile estimators for environmental standards

Environmetrics, 2002
AbstractRecent research has sought to develop a statistically based approach to setting environmental standards, prompted by Barnett and O'Hagan (1997) whose recommendations for a statistically verifiable ideal standard (SVIS) were endorsed by the Royal Commission on Environmental Pollution (1998).
Vic Barnett, Marion Bown
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Ultrasound TDoA positioning using the Best Linear Unbiased Estimator

2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2019
In this paper, a planar positioning technique is proposed, applying the best linear unbiased estimator (BLUE) algorithm to ultrasound time difference of arrival measurements (TDoA). The performance of the proposed approach is validated using numerical simulations and compared to a Least Squares Estimator (LSE).
Antonella Comuniello, Alessio De Angelis, Antonio Moschitta   +2 more
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The best linear unbiased estimator in a singular linear regression model

Statistical Papers, 2016
This paper discusses and employs weighted balanced loss functions. The minimum risk properties of linear estimators of linear model coefficients in the class of linear unbiased estimators are derived. Lower and upper relative efficiencies of the best linear unbiased estimator are presented.
Wu, Jibo, Liu, Chaolin
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Best Linear Unbiased Estimation by Recursive Methods

SIAM Journal on Applied Mathematics, 1966
Introduction. The classical linear estimation problem for a finite number of parameters using least squares dates back to Gauss [1]. In a paper by Aitken [2], the method of parameter estimation was generalized. Instead of obtaining the set of parameters which minimize the sum of squares of the residuals (difference between the observed and expected ...
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Bounds for the difference between a linear unbiased estimate and the best linear unbiased estimate

Physics and Chemistry of the Earth, Part A: Solid Earth and Geodesy, 2000
Abstract Intuitively it is obvious that if a linear unbiased estimator is only “slightly” suboptimal, the estimate cannot differ “much” from the corresponding best linear unbiased estimate for any “reasonable” observation vector. I present a Euclidean, nonstochastic bound which quantifies this heuristic notion.
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