Advances in Complex Data Modeling and Computational Methods in Statistics

Piercesare Secchi

ISBN 10: 3319111485 / ISBN 13: 9783319111483

Published by Springer International Publishing, Springer Nature Switzerland Nov 2014, 2014

About this Item

Neuware -The book is addressed to statisticians working at the forefront of the statistical analysis of complex and high dimensional data and offers a wide variety of statistical models, computer intensive methods and applications: network inference from the analysis of high dimensional data; new developments for bootstrapping complex data; regression analysis for measuring the downsize reputational risk; statistical methods for research on the human genome dynamics; inference in non-euclidean settings and for shape data; Bayesian methods for reliability and the analysis of complex data; methodological issues in using administrative data for clinical and epidemiological research; regression models with differential regularization; geostatistical methods for mobility analysis through mobile phone data exploration. This volume is the result of a careful selection among the contributions presented at the conference 'S.Co.2013: Complex data modeling and computationally intensive methods for estimation and prediction' held at the Politecnico di Milano, 2013. All the papers published here have been rigorously peer-reviewed.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 220 pp. Englisch.

Seller Inventory # 9783319111483

Bibliographic Details

• Title: Advances in Complex Data Modeling and Computational Methods in Statistics
• Publisher: Springer International Publishing, Springer Nature Switzerland Nov 2014
• Publication Date: 2014
• Language: English
• ISBN 10: 3319111485
• ISBN 13: 9783319111483
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 18D millimeters 241 millimeters width by 160 millimeters height by 18 millimeters depth
• Weight: 500 grams

About this title

Synopsis

The book is addressed to statisticians working at the forefront of the statistical analysis of complex and high dimensional data and offers a wide variety of statistical models, computer intensive methods and applications: network inference from the analysis of high dimensional data; new developments for bootstrapping complex data; regression analysis for measuring the downsize reputational risk; statistical methods for research on the human genome dynamics; inference in non-euclidean settings and for shape data; Bayesian methods for reliability and the analysis of complex data; methodological issues in using administrative data for clinical and epidemiological research; regression models with differential regularization; geostatistical methods for mobility analysis through mobile phone data exploration. This volume is the result of a careful selection among the contributions presented at the conference "S.Co.2013: Complex data modeling and computationally intensive methods for estimation and prediction" held at the Politecnico di Milano, 2013. All the papers published here have been rigorously peer-reviewed.