Recent Developments in Multivariate and Random Matrix Analysis: Festschrift in Honour of Dietrich von Rosen

Author:   Thomas Holgersson ,  Martin Singull
Publisher:   Springer Nature Switzerland AG
Edition:   1st ed. 2020
ISBN:  

9783030567750


Pages:   373
Publication Date:   19 September 2021
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Recent Developments in Multivariate and Random Matrix Analysis: Festschrift in Honour of Dietrich von Rosen


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Overview

This volume is a tribute to Professor Dietrich von Rosen on the occasion of his 65th birthday. It contains a collection of twenty original papers. The contents of the papers evolve around multivariate analysis and random matrices with topics such as high-dimensional analysis, goodness-of-fit measures, variable selection and information criteria, inference of covariance structures, the Wishart distribution and growth curve models. 

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Author:   Thomas Holgersson ,  Martin Singull
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   1st ed. 2020
Weight:   0.593kg
ISBN:  

9783030567750


ISBN 10:   3030567753
Pages:   373
Publication Date:   19 September 2021
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

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Thomas Holgersson is Professor of statistics at Linnaeus. He is currently a review panel member of the Swedish research council board and involved in the development of a nationwide graduate school in statistics. He has been working in multiple fields such as econometrics and time series analysis but is now primarily working with random matrix analysis. He is associate editor of Journal of Multivariate Analysis. Martin Singull is Associate Professor in Mathematical Statistic and Head of Division of Mathematical Statistics at the Department of Mathematics, Linköping University. He is also Assistant Director for the Research School in Interdisciplinary Mathematics at Linköping University. Dr Singulls research focuses on supervised learning for Gaussian linear and bilinear (also known as Growth Curve) models with special focus on inference for covariance matrices with various structures.  

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