Shortcuts
Top of page (Alt+0)
Page content (Alt+9)
Page menu (Alt+8)
Your browser does not support javascript, some WebOpac functionallity will not be available.
.
Default
.
PageMenu
-
Main Menu
-
Simple Search
.
Advanced Search
.
Journal Search
.
Refine Search Results
.
Preferences
.
Search Menu
Simple Search
.
Advanced Search
.
New Items Search
.
Journal Search
.
Refine Search Results
.
Bottom Menu
Help
Italian
.
English
.
German
.
New Item Menu
New Items Search
.
New Items List
.
Links
SISSA Library
.
ICTP library
.
Italian National web catalog (SBN)
.
Trieste University web catalog
.
Udine University web catalog
.
© LIBERO v6.4.1sp220816
Page content
You are here
:
Catalogue Card Display
Catalogue Card Display
RAK
Title: Advances in Complex Data Modeling and Computational Methods in Statistics ([EBook]) / edited by Anna Maria Paganoni, Piercesare Secchi. Dewey Class: 519.5 Added Personal Name: Paganoni, Anna Maria. editor. Secchi, Piercesare. editor. Publication: Cham : : Springer International Publishing : : Imprint: Springer,, 2015. Other name(s): SpringerLink (Online service) Physical Details: VIII, 209 p. 41 illus., 27 illus. in color. : online resource. Series: Contributions to Statistics,1431-1968 ISBN: 9783319111490 System details note: Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users). Summary Note: 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.: Contents note: 1 Antonino Abbruzzo, Angelo M. Mineo: Inferring networks from high-dimensional data with mixed variables -- 2 Federico Andreis, Fulvia Mecatti: Rounding Non-integer Weights in Bootstrapping Non-iid Samples: actual problem or harmless practice? -- 3 Marika Arena, Giovanni Azzone, Antonio Conte, Piercesare Secchi, Simone Vantini: Measuring downsize reputational risk in the Oil & Gas industry -- 4 Laura Azzimonti, Marzia A. Cremona, Andrea Ghiglietti, Francesca Ieva, Alessandra Menafoglio, Alessia Pini, Paolo Zanini: BARCAMP Technology Foresight and Statistics for the Future -- 5 Francesca Chiaromonte, Kateryna D. Makova: Using statistics to shed light on the dynamics of the human genome: A review -- 6 Nader Ebrahimi, Ehsan S. Soofi and Refik Soyer: Information Theory and Bayesian Reliability Analysis: Recent Advances -- 7 Stephan F. Huckemann: (Semi-) Intrinsic Statistical Analysis on non-Euclidean Spaces -- 8 John T. Kent: An investigation of projective shape space -- 9 Fabio Manfredini, Paola Pucci, Piercesare Secchi, Paolo Tagliolato, Simone Vantini, Valeria Vitelli: Treelet Decomposition of Mobile Phone Data for Deriving City Usage and Mobility Pattern in the Milan Urban Region -- 10 Cristina Mazzali, Mauro Maistriello, Francesca Ieva, Pietro Barbieri: Methodological issues in the use of administrative databases to study heart failure -- 11 Andrea Mercatant: Bayesian inference for randomized experiments with noncompliance and nonignorable missing data -- 12 Antonio Pulcini, Brunero Liseo: Approximate Bayesian Quantile Regression for Panel Data -- 13 Laura M. Sangalli: Estimating surfaces and spatial fields via regression models with differential regularization. . ------------------------------ *** There are no holdings for this record *** -----------------------------------------------
Quick Search
Search for