Advanced Statistical Methods in Data Science

Ding-Geng Chen

ISBN 10: 9811025932 / ISBN 13: 9789811025938

Verlag: Springer Nature Singapore, Springer Nature Singapore, 2016

Beschreibung

Druck auf Anfrage Neuware - Printed after ordering - This book gathers invited presentations from the 2nd Symposium of the ICSA- CANADA Chapter held at the University of Calgary from August 4-6, 2015. The aim of this Symposium was to promote advanced statistical methods in big-data sciences and to allow researchers to exchange ideas on statistics and data science and to embraces the challenges and opportunities of statistics and data science in the modern world. It addresses diverse themes in advanced statistical analysis in big-data sciences, including methods for administrative data analysis, survival data analysis, missing data analysis, high-dimensional and genetic data analysis, longitudinal and functional data analysis, the design and analysis of studies with response-dependent and multi-phase designs, time series and robust statistics, statistical inference based on likelihood, empirical likelihood and estimating functions. The editorial group selected 14 high-quality presentations from this successful symposium and invitedthe presenters to prepare a full chapter for this book in order to disseminate the findings and promote further research collaborations in this area. This timely book offers new methods that impact advanced statistical model development in big-data sciences. Bestandsnummer des Verkäufers 9789811025938

Bibliografische Details

Titel: Advanced Statistical Methods in Data Science

Verlag: Springer Nature Singapore, Springer Nature Singapore

Erscheinungsdatum: 2016

Einband: Buch

Zustand: Neu

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Críticas:

“This handbook has a good collection of material on useful and interesting topics on data science. The book will be useful to graduate students and researchers interested in gaining perspectives and knowledge on this useful topic. The book comprises a wealth of information, a one-stop shopping, and can be served as a research reference book.” (Technometrics, Vol. 59 (2), April, 2017)