Big and complex data analysis : methodologies and applications [Documento electrónico] / edited by S. Ejaz Ahmed
Language: eng.Country: US - United States of America.Publication: Cham : Springer, 2017Description: XIV, 386 p. : il.ISBN: 978-3-319-41573-4.Series: Contributions to StatisticsSubject - Topical Name: Estatística | Bioestatística | Big data | Análise de dados Online Resources:Click here to access onlineItem type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode | |
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QA276.SPR FCT 97689 Modelling with the master equation, solution methods and applications in social and natural sciences | QA276.SPR FCT 97695 Phase II clinical development of new drugs | QA276.SPR FCT 97702 Statistical modeling for degradation data | QA276.SPR FCT 97739 Big and complex data analysis, methodologies and applications | QA276.SPR FCT 97746 Multivariate methods and forecasting with IBM® SPSS® statistics | QA276.SPR FCT 97747 Functional statistics and related fields | QA276.SPR FCT 97754 Quantitative decisions in drug development |
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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.
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