Springer Big and Complex Data Analysis: Methodologies and Applications (Contributions to Statistics)

This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Its peer-reviewed contributions showcase recent advances in variable selection, estimation and prediction strategies for a host of useful models, as well as essential new developments in the field. The continued and rapid advancement of modern technology now allows scientists to collect data of increasingly unprecedented size and complexity. Examples include epigenomic data, genomic data, proteomic data, high-resolution image data, high-frequency financial data, functional and longitudinal data, and network data. Simultaneous variable selection and estimation is one of the key statistical problems involved in analyzing such big and complex data. The purpose of this book is to stimulate research and foster interaction between researchers in the area of high-dimensional data analysis. More concretely, its goals are to: 1) highlight and expand the breadth of existing methods in big data and high-dimensional data analysis and their potential for the advancement of both the mathematical and statistical sciences; 2) identify important directions for future research in the theory of regularization methods, in algorithmic development, and in methodologies for different application areas; and 3) facilitate collaboration between theoretical and subject-specific researchers. From the Back Cover This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Its peer-reviewed contributions showcase recent advances in variable selection, estimation and prediction strategies for a host of useful models, as well as essential new developments in the field. The continued and rapid advancement of modern technology now allows scientists to collect data of increasingly unprecedented size and complexity. Examples include epigenomic data, genomic data, proteomic data, high-resolution image data, high-frequency financial data, functional and longitudinal data, and network data. Simultaneous variable selection and estimation is one of the key statistical problems involved in analyzing such big and complex data. The purpose of this book is to stimulate research and foster interaction between researchers in the area of high-dimensional data analysis. More concretely, its goals are to: 1) highlight and expand the breadth of existing methods in big data and high-dimensional data analysis and their potential for the advancement of both the mathematical and statistical sciences; 2) identify important directions for future research in the theory of regularization methods, in algorithmic development, and in methodologies for different application areas; and 3) facilitate collaboration between theoretical and subject-specific researchers. About the Author Dr. S. Ejaz Ahmed is Dean of the Faculty of Mathematics and Science and a Professor of Statistics at Brock University. Before joining Brock, he was a professor and head of the Mathematics & Statistics Department at the University of Windsor and University of Regina. Prior to that, he was an assistant professor at the University of Western Ontario. He is an elected fellow of the American Statistical Association and holds many adjunct professorship positions. His areas of expertise include big data analysis, statistical inference, and shrinkage estimation. He has more than 150 published articles in scientific journals and has reviewed more than 100 books. Further, he has written several books, edited and co-edited several volumes and special issues of scientific journals. He has supervised numerous PhD and master’s students and organized several workshops/conferences and many invited sessions. Dr. Ahmed serves on the editorial board of many statistical journals and as a review editor for Technometrics. He served as a Board of Director and Chairman of