Results 251 to 260 of about 88,814 (288)

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 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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Simple least squares estimation versus best linear unbiased prediction

Journal of Statistical Planning and Inference, 1981
Abstract Necessary and sufficient conditions are developed for the simple least squares estimator to coincide with the best linear unbiased predictor. The conditions obtained are valid for a general linear model and are generalizations of the condition given by Watson (1972).
Baksalary, J. K., Kala, R.
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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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Mean driven balance and uniformly best linear unbiased estimators

Statistical Papers, 2014
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zmyślony, Roman, Mexia, João T., Carvalho, Francisco, Sequeira, Inês J.   +3 more
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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.
K. Zhang, X.R. Li
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Best Linear Unbiased Estimation of Location and Scale Parameters

1999
Let us now assume that we have a random sample of size n, X 1, X 2,…, X n , from a three-parameter lognormal distribution [obtained by introducing location and scale parameters in (2.3)] with probability density function $$ \begin{gathered} f(x|\mu ,\sigma ,k) \hfill \\ \,\,\,\,\,\, = \frac{1}{{\left( {{{(k - 1)}^{{\raise0.7ex\hbox{${ - 1 ...
N. Balakrishnan, William W. S. Chen
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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.
Singh, G.N., Prasad, S., Majhi, D.
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Modified best linear unbiased estimator of the shape parameter of log-logistic distribution

Journal of Statistical Computation and Simulation, 2021
Wangxue Chen
exaly