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Best linear unbiased estimators for properties of straight lines

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986
Dorst, L., Smeulders, A.W.M.
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A jackknife approach to estimate the prediction uncertainty from binary classifiers under right-censoring. [PDF]

Stat Methods Med Res
Jahn-Eimermacher A, Klein L, Grieser G.
europepmc   +1 more source

Design-Based Uncertainty for Quasi-Experiments. [PDF]

J Am Stat Assoc
Rambachan A, Roth J.
europepmc   +1 more source

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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 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 ...
Luis M Cruz-Orive
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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 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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