Results 241 to 250 of about 7,710 (298)

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,
Markus Steindl, Mario Huemer, Oliver Lang, Alexander Onic   +3 more
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The best linear unbiased estimator in a singular linear regression model

Statistical Papers, 2016
In this paper, the best linear unbiased estimator of regression coefficients in the singular linear model was considered. Under the weighted balanced loss function the minimum risk properties of linear estimators of regression coefficients in the class of linear unbiased estimators are derived.
Jibo Wu, Chaolin Liu
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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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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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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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A projector oriented approach to the best linear unbiased estimator

Statistical Papers, 2009
The paper provides a projector based approach to the best linear unbiased estimator (BLUE). By revisiting the so called generalized projection operator, introduced in Rao (J R Stat Soc Ser B Stat Methodol 36:442–448, 1974), a number of new formulae for BLUE is established.
Götz Trenkler, Oskar Maria Baksalary
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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).
Comuniello A., De Angelis A., Moschitta A.   +2 more
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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).
Marion Bown, Vic Barnett
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Best linear unbiased estimation for the Weibull process

Microelectronics Reliability, 1994
Abstract Best linear unbiased estimators, approximative simultaneous confidence limits, acceptance regions, and prediction limits are given for the Weibull process. The approach is based on failure terminated observations, the statistic generalized total life, and the logarithmic gamma distribution.
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Best linear unbiased estimators of population variance in successive sampling

Model Assisted Statistics and Applications, 2012
Shakti Prasad, D. Majhi, Garib Nath Singh   +2 more
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