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    Brand new, In English, Fast shipping from London, UK; Tout neuf, en anglais, expédition rapide depuis Londres, Royaume-Uni;ria9783030754938_dbm

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        Présentation Multivariate, Multilinear And Mixed Linear Models de Format Relié

         - Livre Littérature Générale

        Livre Littérature Générale - 01/10/2021 - Relié - Langue : Anglais

        . .

      • Editeur : Springer International Publishing Ag
      • Langue : Anglais
      • Parution : 01/10/2021
      • Format : Moyen, de 350g à 1kg
      • Nombre de pages : 364
      • Expédition : 711
      • Dimensions : 24.1 x 16.0 x 2.6
      • ISBN : 3030754936



      • Résumé :

        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 B?dlewo, Poland, over the last 10 years. Featuring an extensive bibliography of related publications, the book is intended for PhD students and researchers in modern statisticalscience who are interested in multivariate and mixed linear models.

        Biographie:

        Katarzyna Filipiak is an Associate Professor of Mathematics at the Poznan University of Technology, Poland. She has authored 36 peer-reviewed scientific articles, recently focusing on multivariate statistical models. She acts as a reviewer for many statistical journals, is involved in organizing conferences and currently supervises two PhD students. She is an Associate Editor of Communications in Statistics and the Journal of Multivariate Analysis.

        Augustyn Markiewicz is a Professor of Mathematics at the Poznan University of Life Sciences, Poland. He has authored over 60 peer-reviewed articles and has supervised four PhD students. He is an Editorial Board Member of two international journals and has acted as a Guest Editor of several special issues in journals with a high impact factor. He is also involved in organizing international conferences.

        Dietrich von Rosen is a Professor of Statistics at the Swedish University of Agricultural Sciences in Uppsala. He has supervised more than 20 PhD students and written more than 130 peer-reviewed articles, both theoretical and applied works. He has written two advanced books in multivariate analysis, the most recent one appearing in 2018. He is the Editor-in-Chief of the Journal of Multivariate Analysis.?

        Sommaire:

        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 B?dlewo, Poland, over the last 10 years. Featuring an extensive bibliography of related publications, the book is intended for PhD students and researchers in modern statisticalscience who are interested in multivariate and mixed linear models.
        ...