Matrices, Statistics and Big Data Selected Contributions from IWMS 2016 /

• Corporate Author: SpringerLink (Online service)
• Other Authors: Ahmed, S. Ejaz (Editor, http://id.loc.gov/vocabulary/relators/edt), Carvalho, Francisco (Editor, http://id.loc.gov/vocabulary/relators/edt), Puntanen, Simo (Editor, http://id.loc.gov/vocabulary/relators/edt)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Edition: 1st ed. 2019.
• Series: Contributions to Statistics,
• Subjects: Statistics .; Matrix theory.; Algebra.; Data mining.; Probabilities.; Mathematical statistics.; Statistical Theory and Methods.; Linear and Multilinear Algebras, Matrix Theory.; Data Mining and Knowledge Discovery.; Statistics for Life Sciences, Medicine, Health Sciences.; Probability Theory and Stochastic Processes.; Probability and Statistics in Computer Science.
• Physical Description: XII, 190 p. 24 illus., 14 illus. in color. online resource.
• ISBN: 9783030175191
• ISSN: 1431-1968
• DOI: 10.1007/978-3-030-17519-1

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.