CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS
Title | CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS PDF eBook |
Author | Giovanni C. Porzio |
Publisher | Firenze University Press |
Pages | 455 |
Release | |
Genre | Business & Economics |
ISBN | 8855183400 |
The book collects the short papers presented at the 13th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS). The meeting has been organized by the Department of Statistics, Computer Science and Applications of the University of Florence, under the auspices of the Italian Statistical Society and the International Federation of Classification Societies (IFCS). CLADAG is a member of the IFCS, a federation of national, regional, and linguistically-based classification societies. It is a non-profit, non-political scientific organization, whose aims are to further classification research.
Studies in Theoretical and Applied Statistics
Title | Studies in Theoretical and Applied Statistics PDF eBook |
Author | Nicola Salvati |
Publisher | Springer Nature |
Pages | 548 |
Release | 2023-02-14 |
Genre | Mathematics |
ISBN | 3031166094 |
This book includes a wide selection of papers presented at the 50th Scientific Meeting of the Italian Statistical Society (SIS2021), held virtually on 21-25 June 2021. It covers a wide variety of subjects ranging from methodological and theoretical contributions to applied works and case studies, giving an excellent overview of the interests of the Italian statisticians and their international collaborations. Intended for researchers interested in theoretical and empirical issues, this volume provides interesting starting points for further research.
Mathematics and Computation in Music
Title | Mathematics and Computation in Music PDF eBook |
Author | Mariana Montiel |
Publisher | Springer |
Pages | 403 |
Release | 2019-06-11 |
Genre | Computers |
ISBN | 3030213927 |
This book constitutes the thoroughly refereed proceedings of the 7th International Conference on Mathematics and Computation in Music, MCM 2019, held in Madrid, Spain, in June 2019. The 22 full papers and 10 short papers presented were carefully reviewed and selected from 48 submissions. The papers feature research that combines mathematics or computation with music theory, music analysis, composition, and performance. They are organized in topical sections on algebraic and other abstract mathematical approaches to understanding musical objects; remanaging Riemann: mathematical music theory as “experimental philosophy”?; octave division; computer-based approaches to composition and score structuring; models for music cognition and beat tracking; pedagogy of mathematical music theory. The chapter “Distant Neighbors and Interscalar Contiguities” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Multiple Correspondence Analysis and Related Methods
Title | Multiple Correspondence Analysis and Related Methods PDF eBook |
Author | Michael Greenacre |
Publisher | CRC Press |
Pages | 607 |
Release | 2006-06-23 |
Genre | Mathematics |
ISBN | 1420011316 |
As a generalization of simple correspondence analysis, multiple correspondence analysis (MCA) is a powerful technique for handling larger, more complex datasets, including the high-dimensional categorical data often encountered in the social sciences, marketing, health economics, and biomedical research. Until now, however, the literature on the su
Applied Data Mining
Title | Applied Data Mining PDF eBook |
Author | Paolo Giudici |
Publisher | John Wiley & Sons |
Pages | 379 |
Release | 2005-09-27 |
Genre | Computers |
ISBN | 0470871393 |
Data mining can be defined as the process of selection, explorationand modelling of large databases, in order to discover models andpatterns. The increasing availability of data in the currentinformation society has led to the need for valid tools for itsmodelling and analysis. Data mining and applied statistical methodsare the appropriate tools to extract such knowledge from data.Applications occur in many different fields, including statistics,computer science, machine learning, economics, marketing andfinance. This book is the first to describe applied data mining methodsin a consistent statistical framework, and then show how they canbe applied in practice. All the methods described are eithercomputational, or of a statistical modelling nature. Complexprobabilistic models and mathematical tools are not used, so thebook is accessible to a wide audience of students and industryprofessionals. The second half of the book consists of nine casestudies, taken from the author's own work in industry, thatdemonstrate how the methods described can be applied to realproblems. Provides a solid introduction to applied data mining methods ina consistent statistical framework Includes coverage of classical, multivariate and Bayesianstatistical methodology Includes many recent developments such as web mining,sequential Bayesian analysis and memory based reasoning Each statistical method described is illustrated with real lifeapplications Features a number of detailed case studies based on appliedprojects within industry Incorporates discussion on software used in data mining, withparticular emphasis on SAS Supported by a website featuring data sets, software andadditional material Includes an extensive bibliography and pointers to furtherreading within the text Author has many years experience teaching introductory andmultivariate statistics and data mining, and working on appliedprojects within industry A valuable resource for advanced undergraduate and graduatestudents of applied statistics, data mining, computer science andeconomics, as well as for professionals working in industry onprojects involving large volumes of data - such as in marketing orfinancial risk management.
Model-Based Clustering and Classification for Data Science
Title | Model-Based Clustering and Classification for Data Science PDF eBook |
Author | Charles Bouveyron |
Publisher | Cambridge University Press |
Pages | 447 |
Release | 2019-07-25 |
Genre | Mathematics |
ISBN | 1108640591 |
Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observations, and also has open questions about parameter tuning, robustness and uncertainty assessment. This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code; describes modern approaches to high-dimensional data and networks; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-clustering. Written for advanced undergraduates in data science, as well as researchers and practitioners, it assumes basic knowledge of multivariate calculus, linear algebra, probability and statistics.
ASA 2021 Statistics and Information Systems for Policy Evaluation
Title | ASA 2021 Statistics and Information Systems for Policy Evaluation PDF eBook |
Author | Bruno Bertaccini |
Publisher | Firenze University Press |
Pages | 252 |
Release | 2021-12-22 |
Genre | Mathematics |
ISBN | 885518461X |
This book includes 40 peer-reviewed short papers submitted to the Scientific Conference titled Statistics and Information Systems for Policy Evaluation, aimed at promoting new statistical methods and applications for the evaluation of policies and organized by the Association for Applied Statistics (ASA) and the Dept. of Statistics, Computer Science, Applications DiSIA “G. Parenti” of the University of Florence, jointly with the partners AICQ (Italian Association for Quality Culture), AICQ-CN (Italian Association for Quality Culture North and Centre of Italy), AISS (Italian Academy for Six Sigma), ASSIRM (Italian Association for Marketing, Social and Opinion Research), Comune di Firenze, the SIS – Italian Statistical Society, Regione Toscana and Valmon – Evaluation & Monitoring.