Ejaz Ahmed S., Carvalho F., Puntanen S. (eds.) Matrices, Statistics and Big Data: Selected Contributions from IWMS 2016

Springer International Publishing, 2019. — 198 p.This volume features selected, refereed papers on various aspects of statistics, matrix theory and its applications to statistics, as well as related numerical linear algebra topics and numerical solution methods, which are relevant for problems arising in statistics and in big data. The contributions were originally presented at the 25th International Workshop on Matrices and Statistics (IWMS 2016), held in Funchal (Madeira), Portugal on June 6-9, 2016.
The IWMS workshop series brings together statisticians, computer scientists, data scientists and mathematicians, helping them better understand each other’s tools, and fostering new collaborations at the interface of matrix theory and statistics.Front Matter
Further Properties of the Linear Sufficiency in the Partitioned Linear Model
Hybrid Model for Recurrent Event Data
A New Look at Combining Information from Stratum Submodels
Ingram Olkin (1924–2016): An Appreciation for a People Person
A Notion of Positive Definiteness for Arithmetical Functions
Some Issues in Generalized Linear Modeling
Orthogonal Block Structure and Uniformly Best Linear Unbiased Estimators
Hadamard Matrices on Error Detection and Correction: Useful Links to BIBD
Covariance Matrix Regularization for Banded Toeplitz Structure via Frobenius-Norm Discrepancy
Penalized Relative Error Estimation of a Partially Functional Linear Multiplicative Model
High-Dimensional Regression Under Correlated Design: An Extensive Simulation Study
An Efficient Estimation Strategy in Autoregressive Conditional Poisson Model with Applications to Hospital Emergency Department Data