Innovations in Multivariate Statistical Modeling

Preface.- PART 1: Trends in Multi- and Matrix-Variate Analysis.- Q. Guo, X. Deng and N. Ravishanker: Association-based Optimal Subpopulation Selection of Multivariate Data.- T. B. Mattos, L. A. Matos, V. H Lachos Aldo: Likelihood-Based Inference For Linear Mixed-Effects Models With Censored Response Using Skew-Normal Distribution.- Y. Melnykov, M. Perry, V. Melnykov: Robust Estimation of Multiple Change Points in Multivariate Processes.- T. Botha, J. T Ferreira and A. Bekker: Some Computational Aspects Of A Noncentral Dirichlet Family.- Y. Murat Bulut and Olcay Arslan: Modeling Handwritten Digits Dataset Using The Matrix Variate T Distribution.- B. Byukusenge, D. von Rosen and M. Singull: On The Identification Of Extreme Elements In A Residual For The Gmanova-Manova Model.- M. Billio, R. Casarin, M. Costola and M. Iacopini: Matrix-variate Smooth Transition Models for Temporal Networks.- H. Baghishani and J. Ownuk: A Flexible Matrix-Valued Response Regression For Skewed Data.- J. Trink,H. Haghbin and M. Maadooliat: Multivariate Functional Singular Spectrum Analysis: A Nonparametric Approach for Analyzing Functional Time Series.- M. Greenacre: Compositional Data Analysis - Linear Algebra, Visualization And Interpretation.- A. Alzaatreh, F. Famoye and C. Lee: Multivariate Count Data Regression Models And Their Applications.- A. Iranmanesh, M. Rafiei and D. Nagar: A Generalized Multivariate Gamma Distribution.- PART 2: Aspects of High Dimensional Methodology and Bayesian Learning .- G. D' Angella and C. Hennig: A Comparison Of Different Clustering Approaches For High-Dimensional Presence-Absence Data.- S. Millard, M. Arashi and G. Maribe: High-Dimensional Feature Selection For Logistic Regression Using Blended Penalty Functions.- I. Munaweera, S. Muthukumarana and M. Jafari Jozani: A Generalized Quadratic Garrote Approach Towards Ridge Regression Analysis.- M. Roozbeh: High Dimensional Nonlinear Optimization Problem In Semiparametric Regression Model.- PART 3: Frontiers

Andriëtte Bekker is a full professor in Statistics, at the University of Pretoria, South Africa. Her expertise lies in Distribution theory learning, Unsupervised learning, Network learning and Software learning. She is the academic research leader of the Statistical Theory and Applied Statistics focus area within the Department of Science as well as in the Technology/National Research Foundation (DST-NRF) Centre of Excellence in Mathematical and Statistical Sciences. She has published more than 100 peer-reviewed papers in fundamental statistical research. She contributes significantly to the human capacity development of Southern Africa through the multitude of students for whom she acts as supervisor and mentor.

Johannes T. Ferreira is an associate professor in the Department of Statistics, at the University of Pretoria, South Africa, and is Junior Focus Area Coordinator for the Statistical Theory and Applied Statistics focus area of the Centre of Excellence in Mathematical and Statistical Science, based at the University of the Witwatersrand in Johannesburg. He regularly publishes in accredited peer-reviewed journals and reviews manuscripts for international journals. He is an ASLP 4.1/4.2 fellow of Future Africa and has been identified as one of the Top 200 South Africans under the age of 35 by the Mail & Guardian newspaper in the Education category in 2016. 

Mohammad Arashi is an Associate Professor and Director of the Data Science Laboratory at the Ferdowsi University of Mashhad in Iran, and an Extraordinary Professor at the University of Pretoria in South Africa. He is the author of 3 books with Wiley and an elected member of ISI. He has published more than 150 papers and his major includes shrinkage estimation, variable selection, high-dimensional statistics, graphical models, and longitudinal data analysis.

Ding-Geng Chen is an elected fellow of the American Statistical Association and an elected member of the