Название: Data Analysis and Applications 1: New and Classical Approaches Автор: Christos H. Skiadas, James R. Bozeman Издательство: Wiley-ISTE Год: 2019 Страниц: 272 Язык: английский Формат: pdf (true) Размер: 10.17 MB
The data analysis field has been continuously growing over recent decades following the wide applications of computing and data collection along with new developments in analytical tools. Therefore, the need for publications is evident. New publications appear as printed or e-books covering the need for information from all fields of science and engineering, thanks to the wide applicability of data analysis and statistics packages.
This series of books collects a diverse array of work that provides the reader with theoretical and applied information on data analysis methods, models, and techniques, along with appropriate applications.
Volume 1 begins with an introductory chapter by Gilbert Saporta, a leading expert in the field, who summarizes the developments in data analysis over the last 50 years. The book is then divided into three parts: Part 1 presents clustering and regression cases; Part 2 examines grouping and decomposition, GARCH and threshold models, structural equations, and SME modeling; and Part 3 presents symbolic data analysis, time series and multiple choice models, modeling in demography, and data mining.
Part 1. Clustering and Regression 1. Cluster Validation by Measurement of Clustering Characteristics Relevant to the User, Christian Hennig. 2. Histogram-Based Clustering of Sensor Network Data, Antonio Balzanella and Rosanna Verde. 3. The Flexible Beta Regression Model, Sonia Migliorati, Agnese M. Di Brisco and Andrea Ongaro. 4. S-weighted Instrumental Variables, Jan ?mos V??ek.
Part 2. Models and Modeling 5. Grouping Property and Decomposition of Explained Variance in Linear Regression, Henri Wallard. 6. On GARCH Models with Temporary Structural Changes, Norio Watanabe and Fumiaki Okihara. 7. A Note on the Linear Approximation of TAR Models, Francesco Giordano, Marcella Niglio and Cosimo Damiano Vitale. 8. An Approximation of Social Well-Being Evaluation Using Structural Equation Modeling, Leonel Santos-Barrios, Monica Ruiz-Torres, William G?mez-Demetrio, Ernesto S?nchez-Vera, Ana Lorga Da Silva and Francisco Mart?nez-Castaneda. 9. An SEM Approach to Modeling Housing Values, Jim Freeman and Xin Zhao. 10. Evaluation of Stopping Criteria for Ranks in Solving Linear Systems, Benard Abola, Pitos Biganda, Christopher Engstr?m and Sergei Silvestrov. 11. Estimation of a Two-Variable Second-Degree Polynomial via Sampling, Ioanna Papatsouma, Nikolaos Farmakis and Eleni Ketzaki.
Part 3. Estimators, Forecasting and Data Mining 12. Displaying Empirical Distributions of Conditional Quantile Estimates: An Application of Symbolic Data Analysis to the Cost Allocation Problem in Agriculture, Dominique Desbois. 13. Frost Prediction in Apple Orchards Based upon Time Series Models, Monika A. Tomkowicz and Armin O. Schmitt. 14. Efficiency Evaluation of Multiple-Choice Questions and Exams, Evgeny Gershikov and Samuel Kosolapov. 15. Methods of Modeling and Estimation in Mortality, Christos H. Skiadas and Konstantinos N. Zafeiris 16. An Application of Data Mining Methods to the?Analysis of Bank Customer Profitability and Buying Behavior, Pedro Godinho, Joana Dias and Pedro Torres.
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Название: Data Analysis and Applications 1: New and Classical Approaches 