Results 181 to 190 of about 23,412 (229)

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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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

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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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 Estimation and Prediction under a Selection Model

Biometrics, 1975
Mixed linear models are assumed in most animal breeding applications. Convenient methods for computing BLUE of the estimable linear functions of the fixed elements of the model and for computing best linear unbiased predictions of the random elements of the model have been available.
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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 Estimation for the Aitken Model

2020
Recall from Chap. 7 that the least squares estimators of estimable functions are best linear unbiased estimators (BLUEs) of those functions under the Gauss–Markov model. But it turns out that this is not necessarily so under linear models having a more general variance–covariance structure, such as the Aitken model.
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Optimal sensor data quantization for best linear unbiased estimation fusion

2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601), 2004
Distributed estimation is useful for surveillance using sensor networks. Due to the capacity constraints at the communication links, the data from the sensors are transmitted at a rate insufficient to convey all the observations reliably. Therefore, the observations are vector quantized and the estimation is done using the compressed measurements.
Keshu Zhang, X. Rong Li
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