Variance component estimation in linear models

Name: Variance component estimation in linear models
Brand: VDM Verlag
SKU: 9783639268515
Price: 1833 CZK
Availability: InStock

Autor: AliReza Amiri-Simkooei

Jazyk:

Vazba: Brožovaná

Vydavatel: VDM Verlag

Dostupnost: Skladem u dodavatele

Odesíláme za 9-15 dnů

1 833 Kč

Data processing in many fields of applications often relies on the least-squares method, for which a...

Informace o knize

Autor

AliReza Amiri-Simkooei

Jazyk

Angličtina

Vazba

Kniha - Brožovaná

Vydáno

2010

Stránek

252

EAN

9783639268515

ISBN

3639268512

Enbook ID

06834139

Vydavatel

VDM Verlag

Hmotnost

372

Rozměry

152 x 229 x 14

Kompletní popis

Data processing in many fields of applications often relies on the least-squares method, for which a realistic stochastic model of the observables is needed. The estimation of the unknown (co)variance components is generally referred to as variance component estimation (VCE). A review of the existing VCE methods such as MINQUE, BIQUE, and REML is given, and the method of least-squares variance component estimation is in particular discussed. Theoretical and practical aspects of the method are elaborated. In the theoretical viewpoint, an important feature of the method is the capability of applying hypothesis testing to the stochastic model. One can then find an appropriate structure of the stochastic model which includes the relevant noise components into the covariance matrix. In the practical viewpoint, the application of the method to the global positioning system (GPS) observables is presented. Issues like time-correlated noise, satellite elevation dependence, and correlation between different observation types are of particular interest.

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Variance component estimation in linear models

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