Data Science, Classification, and Related Methods, Chikio Hayashi; Keiji Yajima; Hans H. Bock; Noboru

Автор: Richard A. Johnson, Gouri K. Bhattacharyya
Название: Statistics: Principles and Methods, 7th Edition
ISBN: 0470904119 ISBN-13(EAN): 9780470904114
Издательство: Wiley
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Описание: Statistics: Principles and Methods, 7th Edition provides a comprehensive, accurate introduction to statistics for business professionals who need to learn how to apply key concepts. The chapters include real-world data, designed to make the material more relevant. The numerous examples clearly demonstrate the important points of the methods.

Автор: Michael Doumpos; Constantin Zopounidis
Название: Multicriteria Decision Aid Classification Methods
ISBN: 1441952276 ISBN-13(EAN): 9781441952271
Издательство: Springer
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Автор: Fuente, Angel de la.
Название: Mathematical methods and models for economists
ISBN: 0521585295 ISBN-13(EAN): 9780521585293
Издательство: Cambridge Academ
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Описание: This book is intended as a textbook for a first-year PhD course in mathematics for economists and as a reference for graduate students in economics. It provides a self-contained, rigorous treatment of most of the concepts and techniques required to follow the standard first-year theory sequence in micro and macroeconomics.

Автор: Kohler
Название: Data Analysis Using Stata, Third Edition
ISBN: 1597181102 ISBN-13(EAN): 9781597181105
Издательство: Taylor&Francis
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Описание:

Data Analysis Using Stata, Third Edition is a comprehensive introduction to both statistical methods and Stata. Beginners will learn the logic of data analysis and interpretation and easily become self-sufficient data analysts. Readers already familiar with Stata will find it an enjoyable resource for picking up new tips and tricks.

The book is written as a self-study tutorial and organized around examples. It interactively introduces statistical techniques such as data exploration, description, and regression techniques for continuous and binary dependent variables. Step by step, readers move through the entire process of data analysis and in doing so learn the principles of Stata, data manipulation, graphical representation, and programs to automate repetitive tasks. This third edition includes advanced topics, such as factor-variables notation, average marginal effects, standard errors in complex survey, and multiple imputation in a way, that beginners of both data analysis and Stata can understand.

Using data from a longitudinal study of private households, the authors provide examples from the social sciences that are relatable to researchers from all disciplines. The examples emphasize good statistical practice and reproducible research. Readers are encouraged to download the companion package of datasets to replicate the examples as they work through the book. Each chapter ends with exercises to consolidate acquired skills.

Автор: David G. T. Denison
Название: Bayesian Methods for Nonlinear Classification and Regression
ISBN: 0471490369 ISBN-13(EAN): 9780471490364
Издательство: Wiley
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Цена: 20584.00 р.
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Описание: Regression analysis models the relationship between a set of responses and another variable: for example, to estimate the true position of a line through a number of observed points. Unfortunately, data rarely conforms to simple curves and straight lines - parametric models - and this text examines more complex - or nonparametric - models.

Автор: Francesco Mola; Claudio Conversano; Maurizio Vichi
Название: Classification, (Big) Data Analysis and Statistical Learning
ISBN: 3319557076 ISBN-13(EAN): 9783319557076
Издательство: Springer
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Цена: 15372.00 р.
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Описание: 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.

Автор: Simone Borra; Roberto Rocci; Maurizio Vichi; Marti
Название: Advances in Classification and Data Analysis
ISBN: 3540414886 ISBN-13(EAN): 9783540414889
Издательство: Springer
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Цена: 18167.00 р.
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Описание: This volume presents developments in classification and mulitivariate analysis. Topics that have been treated with considerable attention include cluster analysis, discriminant analysis, proximity structure analysis, multidimensional scaling, genetic algorithms, and neural networks.

Автор: Hans-Hermann Bock; Peter Ihm
Название: Classification, Data Analysis, and Knowledge Organization
ISBN: 3540534830 ISBN-13(EAN): 9783540534839
Издательство: Springer
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Цена: 12157.00 р.
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Описание: Proceedings of the 14th Annual Conference of the Gesellschaft fur Klassifikation e.V., University of Marburg March 12-14, 1990

Автор: Maurizio Vichi; Otto Opitz
Название: Classification and Data Analysis
ISBN: 3540656332 ISBN-13(EAN): 9783540656333
Издательство: Springer
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Цена: 18167.00 р.
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Описание: Proceedings of the Biannual Meeting of the Classification Group of Societa Italiana di Statistica (SIS), Pescara, July 3-4, 1997

Автор: Edwin Diday; Yves Lechevallier; Martin Schader; Pa
Название: New Approaches in Classification and Data Analysis
ISBN: 3540584250 ISBN-13(EAN): 9783540584254
Издательство: Springer
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Цена: 15372.00 р.
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Описание: This volume describes the analysis and processing of structural or quantitative data. It emphasizes classification methods and new algorithms, as well as applications in various related fields. It consists of survey papers in addition to research results.