Classification, (big) data analysis and statistical learning
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Bibliographic Information
Classification, (big) data analysis and statistical learning
（Studies in classification, data analysis, and knowledge organization）
Springer, c2018
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Includes bibliographical references
Description and Table of Contents
Description
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.
Table of Contents
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 Niccolo 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. Pappada (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.
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