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Variance component estimation in linear models - AliReza Amiri-Simkooei
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2010, ISBN: 9783639268515

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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. Theoretical and practical aspects on Global Positioning System Buch (fremdspr.) Taschenbuch 01.08.2010 Bücher>Fremdsprachige Bücher>Englische Bücher, VDM, .201

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Variance component estimation in linear models - AliReza Amiri-Simkooei
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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. Theoretical and practical aspects on Global Positioning System Buch (fremdspr.) Bücher>Fremdsprachige Bücher>Englische Bücher, VDM

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Variance component estimation in linear models:Theoretical and practical aspects on Global Positioning System AliReza Amiri-Simkooei Variance component estimation in linear models:Theoretical and practical aspects on Global Positioning System AliReza Amiri-Simkooei Bücher > Wissenschaft > Geowissenschaft, VDM Verlag

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Theoretical and practical aspects on Global Positioning System Theoretical and practical aspects on Global Positioning System Bücher > Wissenschaft > Geowissenschaft, [PU: VDM Verlag Dr. Müller, Saarbrücken]

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Variance component estimation in linear models Theoretical and practical aspects on Global Positioning System - Amiri-Simkooei, AliReza
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Variance component estimation in linear models Theoretical and practical aspects on Global Positioning System - neues Buch

2010, ISBN: 3639268512

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Details zum Buch
Variance component estimation in linear models
Autor:

AliReza Amiri-Simkooei

Titel:
ISBN-Nummer:

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.

Detailangaben zum Buch - Variance component estimation in linear models


EAN (ISBN-13): 9783639268515
ISBN (ISBN-10): 3639268512
Gebundene Ausgabe
Taschenbuch
Erscheinungsjahr: 2010
Herausgeber: VDM Verlag
252 Seiten
Gewicht: 0,389 kg
Sprache: eng/Englisch

Buch in der Datenbank seit 07.03.2008 09:10:44
Buch zuletzt gefunden am 11.08.2018 19:41:56
ISBN/EAN: 9783639268515

ISBN - alternative Schreibweisen:
3-639-26851-2, 978-3-639-26851-5


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