On the Best Quadratic Minimum Bias Non-Negative Estimator of a Two-Variance Component Model
2011 (English)In: Journal of Geodetic Science, ISSN 2081-9943, Vol. 1, no 3, 280-285 p.Article in journal (Refereed) Published
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.
Variance Components, Minimum bias, BQMBNE, Non-negative estimation
Geophysics Probability Theory and Statistics
Research subject SRA - E-Science (SeRC)
IdentifiersURN: urn:nbn:se:kth:diva-61399DOI: 10.2478/v10156-011-0006-yOAI: oai:DiVA.org:kth-61399DiVA: diva2:479030
FunderSwedish e‐Science Research Center
QC 201201192012-01-172012-01-172012-01-19Bibliographically approved