Advanced Statistical Methods in Data Science (1st ed. 2016) (ICSA Book Series in Statistics)

Synopsis

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.

Copyright:

2016

Book Details

Book Quality:

Publisher Quality

ISBN-13:

9789811025945

Related ISBNs:

9789811025938

Publisher:

Springer Singapore, Singapore

Date of Addition:

10/23/19

Copyrighted By:

Springer Singapore, Singapore

Adult content:

No

Language:

English

Has Image Descriptions:

No

Categories:

Nonfiction, Computers and Internet, Business and Finance, Mathematics and Statistics

Submitted By:

Bookshare Staff

Usage Restrictions:

This is a copyrighted book.

Edited by:

Ding-Geng Chen

Edited by:

Jiahua Chen

Edited by:

Xuewen Lu

Edited by:

Grace Y. Yi

Edited by:

Hao Yu