Parameter Estimation and Hypothesis Testing in Linear Models

Karl-Rudolf Koch

ISBN 10: 3540652574 ISBN 13: 9783540652571

Editorial: Springer, 1999

Idioma: Ingl�s

Nuevos Condici�n: New Encuadernaci�n de tapa dura

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Descripci�n

pp. 360 2nd Updated & Enlarged Edition.

N� de ref. del art�culo 26351841

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Detalles bibliogr�ficos

T�tulo: Parameter Estimation and Hypothesis Testing in Linear Models
Editor: Springer
Fecha de publicaci�n: 1999
Idioma: Ingl�s
ISBN 10: 3540652574
ISBN 13: 9783540652571
Encuadernaci�n: Encuadernaci�n de tapa dura
Edici�n: 2� Edici�n
Estado: New

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Sinopsis

A treatment of estimating unknown parameters, testing hypotheses and estimating confidence intervals in linear models. Readers will find here presentations of the Gauss-Markoff model, the analysis of variance, the multivariate model, the model with unknown variance and covariance components and the regression model as well as the mixed model for estimating random parameters. A chapter on the robust estimation of parameters and several examples have been added to this second edition. The necessary theorems of vector and matrix algebra and the probability distributions of test statistics are derived so as to make this book self-contained. Geodesy students as well as those in the natural sciences and engineering will find the emphasis on the geodetic application of statistical models extremely useful.

Rese�a del editor

A treatment of estimating unknown parameters, testing hypotheses and estimating confidence intervals in linear models. Readers will find here presentations of the Gauss-Markoff model, the analysis of variance, the multivariate model, the model with unknown variance and covariance components and the regression model as well as the mixed model for estimating random parameters. A chapter on the robust estimation of parameters and several examples have been added to this second edition. The necessary theorems of vector and matrix algebra and the probability distributions of test statistics are derived so as to make this book self-contained. Geodesy students as well as those in the natural sciences and engineering will find the emphasis on the geodetic application of statistical models extremely useful.

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