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Bilinear regression analysis = an in...

Rosen, Dietrich von.

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Bilinear regression analysis = an introduction /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bilinear regression analysis/ by Dietrich von Rosen.
Reminder of title:	an introduction /
Author:	Rosen, Dietrich von.
Published:	Cham :Springer International Publishing : : 2018.,
Description:	xiii, 468 p. :ill., digital ;24 cm.
[NT 15003449]:	Preface -- Introduction -- The Basic Ideas of Obtaining MLEs: A Known Dispersion -- The Basic Ideas of Obtaining MLEs: Unknown Dispersion -- Basic Properties of Estimators -- Density Approximations -- Residuals -- Testing Hypotheses -- Influential Observations -- Appendices -- Indices.
Contained By:	Springer eBooks
Subject:	Regression analysis. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-78784-8
ISBN:	9783319787848

Bilinear regression analysis = an introduction /
Rosen, Dietrich von.

Bilinear regression analysisan introduction /[electronic resource] :by Dietrich von Rosen. - Cham :Springer International Publishing :2018. - xiii, 468 p. :ill., digital ;24 cm. - Lecture notes in statistics,2200930-0325 ;. - Lecture notes in statistics ;220..

Preface -- Introduction -- The Basic Ideas of Obtaining MLEs: A Known Dispersion -- The Basic Ideas of Obtaining MLEs: Unknown Dispersion -- Basic Properties of Estimators -- Density Approximations -- Residuals -- Testing Hypotheses -- Influential Observations -- Appendices -- Indices.

This book expands on the classical statistical multivariate analysis theory by focusing on bilinear regression models, a class of models comprising the classical growth curve model and its extensions. In order to analyze the bilinear regression models in an interpretable way, concepts from linear models are extended and applied to tensor spaces. Further, the book considers decompositions of tensor products into natural subspaces, and addresses maximum likelihood estimation, residual analysis, influential observation analysis and testing hypotheses, where properties of estimators such as moments, asymptotic distributions or approximations of distributions are also studied. Throughout the text, examples and several analyzed data sets illustrate the different approaches, and fresh insights into classical multivariate analysis are provided. This monograph is of interest to researchers and Ph.D. students in mathematical statistics, signal processing and other fields where statistical multivariate analysis is utilized. It can also be used as a text for second graduate-level courses on multivariate analysis.

ISBN: 9783319787848

Standard No.: 10.1007/978-3-319-78784-8doiSubjects--Topical Terms:

529831
Regression analysis.

LC Class. No.: QA278.2 / .R674 2018

Dewey Class. No.: 519.536

Bilinear regression analysis = an introduction /
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100 1 $a Rosen, Dietrich von. $3 3338882
245 1 0 $a Bilinear regression analysis $h [electronic resource] : $b an introduction / $c by Dietrich von Rosen.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a xiii, 468 p. : $b ill., digital ; $c 24 cm.
490 1 $a Lecture notes in statistics, $x 0930-0325 ; $v 220
505 0 $a Preface -- Introduction -- The Basic Ideas of Obtaining MLEs: A Known Dispersion -- The Basic Ideas of Obtaining MLEs: Unknown Dispersion -- Basic Properties of Estimators -- Density Approximations -- Residuals -- Testing Hypotheses -- Influential Observations -- Appendices -- Indices.
520 $a This book expands on the classical statistical multivariate analysis theory by focusing on bilinear regression models, a class of models comprising the classical growth curve model and its extensions. In order to analyze the bilinear regression models in an interpretable way, concepts from linear models are extended and applied to tensor spaces. Further, the book considers decompositions of tensor products into natural subspaces, and addresses maximum likelihood estimation, residual analysis, influential observation analysis and testing hypotheses, where properties of estimators such as moments, asymptotic distributions or approximations of distributions are also studied. Throughout the text, examples and several analyzed data sets illustrate the different approaches, and fresh insights into classical multivariate analysis are provided. This monograph is of interest to researchers and Ph.D. students in mathematical statistics, signal processing and other fields where statistical multivariate analysis is utilized. It can also be used as a text for second graduate-level courses on multivariate analysis.
650 0 $a Regression analysis. $3 529831
650 1 4 $a Statistics. $3 517247
650 2 4 $a Statistical Theory and Methods. $3 891074
650 2 4 $a Linear and Multilinear Algebras, Matrix Theory. $3 891082
650 2 4 $a Statistics for Life Sciences, Medicine, Health Sciences. $3 891086
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710 2 $a SpringerLink (Online service) $3 836513
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830 0 $a Lecture notes in statistics ; $v 220. $3 3338883
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-78784-8
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