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Advanced Statistical Methods in Data Science

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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 invited the 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.

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Detail Information

Series Title

ICSA Book Series in Statistics

Call Number

332 ADV a

Publisher

: Springer., 2016

Collation

XVI, 222

Language

English

ISBN/ISSN

978-981-10-2594-5

Classification

332

Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

Ed. 1

Subject(s)

Finance
Management, Economics

Specific Detail Info

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Statement of Responsibility

Editor: Ding-Geng Chen...[et al.]

Other Information

Cataloger

umi

Source

https://link.springer.com/content/pdf/10.1007/978-981-10-2594-5.pdf?pdf=button

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