Matrices, Statistics and Big Data
Selected Contributions from IWMS 2016
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Beschreibung
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.
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.
Presents the latest advances in matrix theory and statistics Includes methods for solving big data problems Features contributions by leading experts in the area
Autor*in
S. Ejaz Ahmed
Themen in »Matrices, Statistics and Big Data«
Statistical models Matrix theory Linear algebra Big data analytics Numerical linear algebra Multivariate linear models Matrix inequalities Experimental designs Correlated and complex data High-dimensional data analysis Shrinkage estimation strategy