Results 241 to 250 of about 7,836,569 (290)

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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A note on best linear unbiased estimation in the restricted general linear model

Statistics, 1983
Summary: \textit{J. K. Baksalary} and \textit{R. Kala} [ibid. 10, 27-35 (1979; Zbl 0416.62049)] considered the problem of best linear unbiased estimation in the restricted general linear model and gave a necessary and sufficient condition for the BLUE of every estimable parametric function under the unrestricted model to be its BLUE under the ...

exaly   +3 more sources

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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On Best Linear Unbiased Estimation in the Restricted General Linear Model

Statistics, 1987
YANG WENLI, CUI HENGJIAN, SUN GUOWEN
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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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Best linear unbiased estimators of population variance in successive sampling

Model Assisted Statistics and Applications, 2012
In present work, best linear unbiased estimators have been proposed to estimate the population variance on current occasion in two-occasion successive (rotation) sampling. Optimum replacement policies of the proposed estimators are discussed. Results are supported with the suitable empirical studies.
Garib Nath Singh, Shakti Prasad, D. Majhi   +2 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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Transformation and reparametrization for best linear unbiased estimation of a linear model with arbitrary known variance

Communications in Statistics Part B: Simulation and Computation, 1986
Two approaches to the problem of best linear unbiased estimation and testing of a linear hypothesis in a linear model under known arbitrary variance are presented which lead directly to efficient and numerically stable algorithms for the computation of ...
Dunne, T.T., Schall, R.
exaly   +1 more source

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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