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Journal of statistical computation and simulation, 2016-06, Vol.86 (9), p.1798-1817
2016
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Details

Autor(en) / Beteiligte
Titel
Enhanced design efficiency through least upper bounds
Ist Teil von
  • Journal of statistical computation and simulation, 2016-06, Vol.86 (9), p.1798-1817
Ort / Verlag
Abingdon: Taylor & Francis
Erscheinungsjahr
2016
Quelle
Alma/SFX Local Collection
Beschreibungen/Notizen
  • Lower and upper spectral bounds are known for positive-definite matrices in under Loewner (Uber monotone Matrixfunktionen. Math Z. 1934;38:177-216) ordering. Lower and upper singular bounds for matrices of order in derive under an induced ordering. These orderings are combined here to the following effects. Given two first-order experimental designs in their upper singular bound enhances both and in that its Fisher Information matrix dominates those for both and thus ordering essentials in Gauss-Markov estimation. Moreover, if and are dispersion matrices for linear estimators under and respectively, then is the spectral lower bound for in . In essence this algorithm identifies elements in complementary to those of and combines these into . Case studies illustrate gains to be made thereby in first and second-order designs. Specifically, two examples demonstrate that designs optimal under separate criteria may be combined into a single design dominating both. In addition, selected examples demonstrate that classical second-order designs may be improved inter se.
Sprache
Englisch
Identifikatoren
ISSN: 0094-9655
eISSN: 1563-5163
DOI: 10.1080/00949655.2015.1082133
Titel-ID: cdi_informaworld_taylorfrancis_310_1080_00949655_2015_1082133

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