Advances in Complex Data Modeling and Computational Methods in Statistics

Piercesare Secchi

ISBN 10: 3319111485 / ISBN 13: 9783319111483

Edité par Springer International Publishing, Springer Nature Switzerland Nov 2014, 2014

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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. N° de réf. du vendeur 9783319111483

Détails bibliographiques

Titre : Advances in Complex Data Modeling and ...

Éditeur : Springer International Publishing, Springer Nature Switzerland Nov 2014

Date d'édition : 2014

Reliure : Buch

Etat : Neu

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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.