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Classification, (Big) Data Analysis and Statistical Learning

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Classification, (Big) Data Analysis and Statistical Learning/ edited by Francesco Mola, Claudio Conversano, Maurizio Vichi.
其他作者:	Mola, Francesco.
面頁冊數:	XVI, 242 p. 65 illus., 21 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Statistics . -
電子資源:	https://doi.org/10.1007/978-3-319-55708-3
ISBN:	9783319557083

Classification, (Big) Data Analysis and Statistical Learning
Classification, (Big) Data Analysis and Statistical Learning[electronic resource] /edited by Francesco Mola, Claudio Conversano, Maurizio Vichi. - 1st ed. 2018. - XVI, 242 p. 65 illus., 21 illus. in color.online resource. - Studies in Classification, Data Analysis, and Knowledge Organization,1431-8814. - Studies in Classification, Data Analysis, and Knowledge Organization,.

Rank Properties for Centred Three-way Arrays – C. Albers (Univ. of Groningen) et al -- Principal Component Analysis of Complex Data and Application to Climatology – S. Camiz (La Sapienza Univ. of Rome) et al -- Clustering upper level units in multilevel models for ordinal data – L. Grilli (Univ. of Florence) et al -- A Multilevel Heckman Model To Investigate Financial Assets Among Old People In Europe – O. Paccagnella (univ. of Padua) et al -- Multivariate stochastic downscaling with semicontinuous data – L. Paci (univ. of Bologna) et al -- Motivations and expectations of students’ mobility abroad: a mapping technique – V. Caviezel (Univ. of Bergamo) et al -- Comparing multi-step ahead forecasting functions for time series clustering – M. Corduas (Univ. of Naples Federico II) et al -- Electre Tri-Machine Learning Approach to the Record Linkage – V. Minnetti (La Sapienza Univ. of Rome) et al -- . MCA Based Community Detection – C. Drago (Univ. of Rome Niccolò Cusano) -- Classi fying social roles by network structures – S. Gozzo (univ. of Catania) et al -- Bayesian Networks For Financial Markets Signals Detection – A. Greppi (univ.of Pavia) et al -- Finite sample behaviour of MLE in network autocorrelation models – M. La Rocca (Univ. of Salerno) et al -- Classification Models as Tools of Bankruptcy Prediction – Polish Experience – J. Pochiecha (Cracow university) et al -- Clustering macroseismic fields by statistical data depth functions – C. Agostinelli (Univ. of Trento) -- Depth based tests for circular antipodal symmetry – G. Pandolfo (Univ. of Cassino) et al -- Estimating The Effect Of Prenatal Care On Birth Outcomes – E. Sironi (Sacro Cuore University) et al -- Bifurcations And Sunspots In Continuous Time Optimal Models With Externalities – B.Venturi (Univ. of Cagliari) et al -- Enhancing Big Data Exploration with Faceted Browsing – S. Bergamaschi (Univ. of Modena and Reggio Emilia) et al -- Big data meet pharmaceutical industry: an application on social media data – C. Liberati (Univ. of Milan Bicocca) et al -- From Big Data to information: statistical issues through a case study – S. Signorelli (Univ. of Bergamo) et al -- Quality of Classification approaches for the quantitative analysis of international conflict – A.F.X. Wilhelm (Jacobs Univ. Bremen) -- P-splines based clustering as a general framework: some applications using different clustering algorithms – C. Iorio (Univ. of Naples Federico II) et al -- A graphical copula-based tool for detecting tail dependence – R. Pappadà (univ. of Trieste) et al -- Comparing spatial and spatio-temporal FPCA to impute large continuous gaps in space – M. Ruggeri (Univ. of Palermo) et al -- Exploring Italian students’ performances in the SNV test: a quantile regression perspective – A. Costanzo (National Institute for the Evaluation of Education and Training – INVALSI) et al. .

This edited book focuses on the latest developments in classification, statistical learning, data analysis and related areas of data science, including statistical analysis of large datasets, big data analytics, time series clustering, integration of data from different sources, as well as social networks. It covers both methodological aspects as well as applications to a wide range of areas such as economics, marketing, education, social sciences, medicine, environmental sciences and the pharmaceutical industry. In addition, it describes the basic features of the software behind the data analysis results, and provides links to the corresponding codes and data sets where necessary. This book is intended for researchers and practitioners who are interested in the latest developments and applications in the field. The peer-reviewed contributions were presented at the 10th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society, held in Santa Margherita di Pula (Cagliari), Italy, October 8–10, 2015.

ISBN: 9783319557083

Standard No.: 10.1007/978-3-319-55708-3doiSubjects--Topical Terms:

1253516
Statistics .

LC Class. No.: QA276-280

Dewey Class. No.: 519.5

Classification, (Big) Data Analysis and Statistical Learning
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