Data Analysis and Applications 1


Clustering and Regression, Modeling-estimating, Forecasting and Data Mining


Volume 2 – Big Data, Artificial Intelligence and Data Analysis SET Coordinated by Jacques Janssen

Data Analysis and Applications 1

Edited by

Christos H. Skiadas, Technical University of Crete, Greece
James R. Bozeman, American University of Malta


ISBN : 9781786303820

Publication Date : February 2019

Hardcover 286 pp

145.00 USD

Co-publisher

Description


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.

Contents


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 Ámos Víšek.

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 Gómez-Demetrio, Ernesto Sánchez-Vera, Ana Lorga Da Silva and Francisco Martínez-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 Engström 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.

About the authors


Christos H. Skiadas is the Founder and former Director of the Data Analysis and Forecasting Laboratory at the Technical University of Crete, Greece. He continues his work at the university at the ManLab in the Department of Production Engineering and Management.

James R. Bozeman holds a PhD in Mathematics from Dartmouth College, USA, and is Professor of Mathematics at the American University of Malta.