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Recent Developments in Multivariate and Random Matrix Analysis

Festschrift in Honour of Dietrich von Rosen

  • Book
  • © 2020

Overview

Editors:
  1. Thomas Holgersson

    1. Department of Economics and Statistics, Linnaeus University, Växjö, Sweden

  2. Martin Singull

    1. Department of Mathematics, Linköping University, Linköping, Sweden

  • Describes recent developments in multivariate statistical analysis
  • Contains contributions on high-dimensional data analysis
  • Written by experts in the field and dedicated to Dietrich von Rosen

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About this book

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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Table of contents (20 chapters)

  1. Front Matter

    Pages i-xiii
    Download chapter PDF

  2. Spectral Analysis of Large Reflexive Generalized Inverse and Moore-Penrose Inverse Matrices

    • Taras Bodnar, Nestor Parolya
    Pages 1-16

  3. Testing for Double Complete Symmetry

    • Carlos A. Coelho, Martin Singull
    Pages 17-39

  4. Convexity of Sets Under Normal Distribution in the Structural Alloy Steel Standard

    • Kai-Tai Fang, Zhen Luo, Yung Liang Tong
    Pages 41-49

  5. Comments on Maximum Likelihood Estimation and Projections Under Multivariate Statistical Models

    • Katarzyna Filipiak, Mateusz John, Augustyn Markiewicz
    Pages 51-66

  6. Growth Curve Model with Orthogonal Covariance Structure

    • Miguel Fonseca, Martin Singull
    Pages 67-81

  7. Holonomic Gradient Method for the Cumulative Distribution Function of the Largest Eigenvalue of a Complex Wishart Matrix with Noncentrality Matrix of Rank One

    • Yuta Fukasawa, Akimichi Takemura
    Pages 83-101

  8. Some Tests for the Extended Growth Curve Model and Applications in the Analysis of Clustered Longitudinal Data

    • Jemila S. Hamid, Sayantee Jana
    Pages 103-122

  9. Properties of BLUEs and BLUPs in Full vs. Small Linear Models with New Observations

    • Stephen J. Haslett, Augustyn Markiewicz, Simo Puntanen
    Pages 123-146

  10. A Collection of Moments of the Wishart Distribution

    • Thomas Holgersson, Jolanta Pielaszkiewicz
    Pages 147-162

  11. Risk and Bias in Portfolio Optimization

    • Thomas Holgersson, Martin Singull
    Pages 163-173

  12. Approximating Noncentral Chi-Squared to the Moments and Distribution of the Likelihood Ratio Statistic for Multinomial Goodness of Fit

    • Björn Holmquist, Anna Sjöström, Sultana Nasrin
    Pages 175-198

  13. Covariance Structure Tests for t-distribution

    • Tõnu Kollo, Marju Valge
    Pages 199-217

  14. Variable Selection in Joint Mean and Covariance Models

    • Chaofeng Kou, Jianxin Pan
    Pages 219-244

  15. On Shrinkage Estimators and “Effective Degrees of Freedom”

    • Lynn R. LaMotte, Julia Volaufova, Simo Puntanen
    Pages 245-254

  16. On Explicit Estimation of the Growth Curve Model with a Block Circular Covariance Structure

    • Yuli Liang, Deliang Dai
    Pages 255-266

  17. Space Decomposition and Estimation in Multivariate Linear Models

    • Joseph Nzabanita, Innocent Ngaruye
    Pages 267-286

  18. Detection of Sparse and Weak Effects in High-Dimensional Feature Space, with an Application to Microbiome Data Analysis

    • Tatjana Pavlenko, Annika Tillander, Justine Debelius, Fredrik Boulund
    Pages 287-311

  19. Exploring Consistencies of Information Criterion and Test-Based Criterion for High-Dimensional Multivariate Regression Models Under Three Covariance Structures

    • Tetsuro Sakurai, Yasunori Fujikoshi
    Pages 313-334

  20. Mean Value Test for Three-Level Multivariate Observations with Doubly Exchangeable Covariance Structure

    • Ivan Žežula, Daniel Klein, Anuradha Roy
    Pages 335-349
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Editors and Affiliations

  • Department of Economics and Statistics, Linnaeus University, Växjö, Sweden

    Thomas Holgersson

  • Department of Mathematics, Linköping University, Linköping, Sweden

    Martin Singull

About the editors

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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