Recent Developments in Multivariate and Random Matrix Analysis

- Spectral Analysis of Large Reflexive Generalized Inverse and Moore-Penrose Inverse Matrices. - Testing for Double Complete Symmetry. - Convexity of Sets Under Normal Distribution in the Structural Alloy Steel Standard. - Comments on Maximum Likelihood Estimation and Projections Under Multivariate Statistical Models. - Growth Curve Model with Orthogonal Covariance Structure. - Holonomic Gradient Method for the Cumulative Distribution Function of the Largest Eigenvalue of a Complex Wishart Matrix with NoncentralityMatrix of Rank One. - Some Tests for the Extended Growth Curve Model and Applications in the Analysis of Clustered Longitudinal Data. - Properties of BLUEs and BLUPs in Full vs. Small Linear Models with New Observations. - A Collection of Moments of theWishart Distribution. - Risk and Bias in Portfolio Optimization. - Approximating Noncentral Chi-Squared to the Moments and Distribution of the Likelihood Ratio Statistic for Multinomial Goodness of Fit. - Covariance Structure Tests for t -distribution. - Variable Selection in Joint Mean and Covariance Models. - On Shrinkage Estimators and "Effective Degrees of Freedom". - On Explicit Estimation of the Growth Curve Model with a Block Circular Covariance Structure. - Space Decomposition and Estimation in Multivariate Linear Models. - Detection of Sparse and Weak Effects in High-Dimensional Feature Space, with an Application to Microbiome Data Analysis. - Exploring Consistencies of Information Criterion and Test-Based Criterion for High-Dimensional Multivariate Regression Models Under Three Covariance Structures. - Mean Value Test for Three-Level Multivariate Observations with Doubly Exchangeable Covariance Structure. - Estimation of the Common Mean of Two Multivariate Normal Distributions Under Symmetrical and Asymmetrical Loss Functions.

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.