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On the Best Quadratic Minimum Bias Non-Negative Estimator of a Two-Variance Component Model
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatik och Geodesi. (Geodesy)
2011 (English)In: Journal of Geodetic Science, ISSN 2081-9943, Vol. 1, no 3, 280-285 p.Article in journal (Refereed) Published
Abstract [en]

Variance components (VCs) in linear adjustment models are usually successfully computed by unbiased estimators. However, for many unbiased VC techniques estimated variance components might be negative, a result that cannot be tolerated by the user. This is, for example, the case with the simple additive VC model aσ2/1 + bσ2/2 with known coefficients a and b, where either of the unbiasedly estimated variance components σ2/1 + σ2/2 may frequently come out negative. This fact calls for so-called non-negative VC estimators. Here the Best Quadratic Minimum Bias Non-negative Estimator (BQMBNE) of a two-variance component model is derived. A special case with independent observations is explicitly presented.

Place, publisher, year, edition, pages
Poland: Versita , 2011. Vol. 1, no 3, 280-285 p.
Keyword [en]
Variance Components, Minimum bias, BQMBNE, Non-negative estimation
National Category
Geophysics Probability Theory and Statistics
Research subject
SRA - E-Science (SeRC)
Identifiers
URN: urn:nbn:se:kth:diva-61399DOI: 10.2478/v10156-011-0006-yOAI: oai:DiVA.org:kth-61399DiVA: diva2:479030
Funder
Swedish e‐Science Research Center
Note
QC 20120119Available from: 2012-01-17 Created: 2012-01-17 Last updated: 2012-01-19Bibliographically approved

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Publisher's full texthttp://versita.metapress.com/content/j5145142pt18v953/?p=ad2c512848e545a280254fcb19b46e8b&pi=9

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  • apa
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