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Multivariate, multilinear and mixed ...

Filipiak, Katarzyna.

Multivariate, multilinear and mixed linear models

Record Type:	Electronic resources : Monograph/item
Title/Author:	Multivariate, multilinear and mixed linear modelsedited by Katarzyna Filipiak, Augustyn Markiewicz, Dietrich von Rosen.
other author:	Filipiak, Katarzyna.
Published:	Cham :Springer International Publishing :2021.
Description:	xii, 350 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Multivariate analysis
Online resource:	https://doi.org/10.1007/978-3-030-75494-5
ISBN:	9783030754945$q(electronic bk.)

Multivariate, multilinear and mixed linear models
Multivariate, multilinear and mixed linear models[electronic resource] /edited by Katarzyna Filipiak, Augustyn Markiewicz, Dietrich von Rosen. - Cham :Springer International Publishing :2021. - xii, 350 p. :ill. (some col.), digital ;24 cm. - Contributions to statistics. - Contributions to statistics..

Preface -- Holonomic gradient method for multivariate distribution theory (Akimichi Takemura) -- From normality to skewed multivariate distributions: a personal view (Tonu Kollo) -- Multivariate moments in multivariate analysis (Jolanta Pielaszkiewicz and Dietrich von Rosen) -- Regularized estimation of covariance structure through quadratic loss function (Defei Zhang, Xiangzhao Cui, Chun Li, Jine Zhao, Li Zeng, and Jianxin Pan) -- Separable covariance structure identification for doubly multivariate data (Katarzyna Filipiak, Daniel Klein, and Monika Mokrzycka) -- Estimation and testing of the covariance structure of doubly multivariate data (Katarzyna Filipiak and Daniel Klein) -- Testing equality of mean vectors with block-circular and block compound-symmetric covariance matrices (Carlos A. Coelho) -- Estimation and testing hypotheses in two-level and three-level multivariate data with block compound symmetric covariance structure (Arkadiusz Koziol, Anuradha Roy, Roman Zmyslony, Ivan Zezula, and Miguel Fonseca) -- Testing of multivariate repeated measures data with block exchangeable covariance structure (Ivan Zezula, Daniel Klein, and Anuradha Roy) -- On a simplified approach to estimation in experiments with orthogonal block structure (Radoslaw Kala) -- A review of the linear sufficiency and linear prediction sufficiency in the linear model with new observations (Stephen J. Haslett, Jarkko Isotalo, Radoslaw Kala, Augustyn Markiewicz, and Simo Puntanen) -- Linear mixed-effects model using penalized spline based on data transformation methods (Syed Ejaz Ahmed, Dursun Aydin and Ersin Yilmaz) -- MMLM meetings - List of Publications -- Index.

This book presents the latest findings on statistical inference in multivariate, multilinear and mixed linear models, providing a holistic presentation of the subject. It contains pioneering and carefully selected review contributions by experts in the field and guides the reader through topics related to estimation and testing of multivariate and mixed linear model parameters. Starting with the theory of multivariate distributions, covering identification and testing of covariance structures and means under various multivariate models, it goes on to discuss estimation in mixed linear models and their transformations. The results presented originate from the work of the research group Multivariate and Mixed Linear Models and their meetings held at the Mathematical Research and Conference Center in Bedlewo, Poland, over the last 10 years. Featuring an extensive bibliography of related publications, the book is intended for PhD students and researchers in modern statistical science who are interested in multivariate and mixed linear models.

ISBN: 9783030754945$q(electronic bk.)

Standard No.: 10.1007/978-3-030-75494-5doiSubjects--Topical Terms:

217219
Multivariate analysis

LC Class. No.: QA278

Dewey Class. No.: 519.535

Multivariate, multilinear and mixed linear models
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245 0 0 $a Multivariate, multilinear and mixed linear models $h [electronic resource] / $c edited by Katarzyna Filipiak, Augustyn Markiewicz, Dietrich von Rosen.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a xii, 350 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Contributions to statistics
505 0 $a Preface -- Holonomic gradient method for multivariate distribution theory (Akimichi Takemura) -- From normality to skewed multivariate distributions: a personal view (Tonu Kollo) -- Multivariate moments in multivariate analysis (Jolanta Pielaszkiewicz and Dietrich von Rosen) -- Regularized estimation of covariance structure through quadratic loss function (Defei Zhang, Xiangzhao Cui, Chun Li, Jine Zhao, Li Zeng, and Jianxin Pan) -- Separable covariance structure identification for doubly multivariate data (Katarzyna Filipiak, Daniel Klein, and Monika Mokrzycka) -- Estimation and testing of the covariance structure of doubly multivariate data (Katarzyna Filipiak and Daniel Klein) -- Testing equality of mean vectors with block-circular and block compound-symmetric covariance matrices (Carlos A. Coelho) -- Estimation and testing hypotheses in two-level and three-level multivariate data with block compound symmetric covariance structure (Arkadiusz Koziol, Anuradha Roy, Roman Zmyslony, Ivan Zezula, and Miguel Fonseca) -- Testing of multivariate repeated measures data with block exchangeable covariance structure (Ivan Zezula, Daniel Klein, and Anuradha Roy) -- On a simplified approach to estimation in experiments with orthogonal block structure (Radoslaw Kala) -- A review of the linear sufficiency and linear prediction sufficiency in the linear model with new observations (Stephen J. Haslett, Jarkko Isotalo, Radoslaw Kala, Augustyn Markiewicz, and Simo Puntanen) -- Linear mixed-effects model using penalized spline based on data transformation methods (Syed Ejaz Ahmed, Dursun Aydin and Ersin Yilmaz) -- MMLM meetings - List of Publications -- Index.
520 $a This book presents the latest findings on statistical inference in multivariate, multilinear and mixed linear models, providing a holistic presentation of the subject. It contains pioneering and carefully selected review contributions by experts in the field and guides the reader through topics related to estimation and testing of multivariate and mixed linear model parameters. Starting with the theory of multivariate distributions, covering identification and testing of covariance structures and means under various multivariate models, it goes on to discuss estimation in mixed linear models and their transformations. The results presented originate from the work of the research group Multivariate and Mixed Linear Models and their meetings held at the Mathematical Research and Conference Center in Bedlewo, Poland, over the last 10 years. Featuring an extensive bibliography of related publications, the book is intended for PhD students and researchers in modern statistical science who are interested in multivariate and mixed linear models.
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650 2 4 $a Linear Algebra. $3 821215
650 2 4 $a Applied Statistics. $3 805583
700 1 $a Filipiak, Katarzyna. $3 907343
700 1 $a Markiewicz, Augustyn. $3 907344
700 1 $a Rosen, Dietrich von. $3 234807
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