Matrices, Statistics and Big Data [electronic resource] : Selected Contributions from IWMS 2016 /
Ahmed, S. Ejaz.editor.
Carvalho, Francisco.editor.
Puntanen, Simo.editor.
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.
Preface (S. Ejaz Ahmed, Francisco Carvalho, Simo Puntanen) -- Further properties of the linear sufficiency in the partitioned linear model (Augustyn Markiewicz, Simo Puntanen) -- Hybrid model for recurrent event data (Ivo Sousa-Ferreira, Ana Maria Abreu) -- A new look at combining information from stratum submodels (Radosław Kala) -- Ingram Olkin (1924–2016): An appreciation for a people person (Simo Puntanen, George P. H. Styan) -- A notion of positive definiteness for arithmetical functions (Mika Mattila, Pentti Haukkanen) -- Some issues in generalized linear modeling (Alan Agresti) -- Orthogonal block structure and uniformly best linear unbiased estimators (Sandra S. Ferreira, Dário Ferreira, Célia Nunes, Francisco Carvalho, João Tiago Mexia) -- Hadamard matrices on error detection and correction: Useful links to BIBD (Carla Francisco, Teresa A. Oliveira, Amílcar Oliveira, Francisco Carvalho) -- Covariance matrix regularization for banded Toeplitz-structure via Frobenius-norm discrepancy (Xiangzhao Cui, Zhenyang Li, Jine Zhao, Defei Zhang, Jianxin Pan) -- Penalized relative error estimation of a partially functional linear multiplicative model (Tao Zhang, Yuan Huang, Qingzhao Zhang, Shuangge Ma, S. Ejaz Ahmed) -- High-dimensional regression under correlated design: An extensive simulation study (S. Ejaz Ahmed, Hwanwoo Kim, Gökhan Yıldırım and Bahadır Yüzbaşı) -- An efficient estimation strategy in autoregressive conditional Poisson model with applications to hospital emergency department data (S. Ejaz Ahmed, Khalifa Es-Sebaiy, Abdulkadir Hussein, Idir Ouassou, Anne Snowdon). .
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.
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.