Table of Contents

Cover

Preface

Introduction

PART 1: Applications

1 Context-specific Independence in Innovation Study
1. 1.1. Introduction
2. 1.2. Parametrization for CS independencies
3. 1.3. Stratified chain graph models
4. 1.4. Application on real data
5. 1.5. Conclusion
6. 1.6. References
2 Analysis of the Determinants and Outputs of Innovation in the Nordic Countries
1. 2.1. Introduction
2. 2.2. Innovation
3. 2.3. Methodology
4. 2.4. Results
5. 2.5. Conclusion
6. 2.6. References
3 Bibliometric Variables Determining the Quality of a Dentistry Journal
1. 3.1. Introduction
2. 3.2. Statistical methodology
3. 3.3. Results
4. 3.4. Conclusions
5. 3.5. Acknowledgment
6. 3.6. References
4 Analysis of Dependence among Growth Rates of GDP of V4 Countries Using Four-dimensional Vine Copulas
1. 4.1. Introduction
2. 4.2. Theory
3. 4.3. Results
4. 4.4. Conclusion and future work
5. 4.5. Acknowledgment
6. 4.6. References
5 Monitoring the Compliance of Countries on Emissions Mitigation Using Dissimilarity Indices
1. 5.1. Introduction
2. 5.2. The proposed method
3. 5.3. Application of method
4. 5.4. Conclusions
5. 5.5. Appendix
6. 5.6. References
6 Maximum Entropy and Distributions of Five-Star Ratings
1. 6.1. Introduction
2. 6.2. Entropy framework to five-star ratings
3. 6.3. Maximum entropy of ratings for values k = 1, 2, 3, ..., 30
4. 6.4. Application to real five-star rating data
5. 6.5. Conclusions
6. 6.6. References

PART 2: The Impact of the Economic and Financial Crisis in Europe

7 Access to Credit for SMEs after the 2008 Financial Crisis: The Northern Italian Perspective
1. 7.1. Introduction
2. 7.2. Italian SMEs and access to credit
3. 7.3. The data
4. 7.4. Methodology
5. 7.5. Analysis and discussion
6. 7.6. Conclusion
7. 7.7. References
8 Gender-Based Differences in the Impact of the Economic Crisis on Labor Market Flows in Southern Europe
1. 8.1. Introduction
2. 8.2. Data, methods and limitations
3. 8.3. Results
4. 8.4. Conclusions and discussion
5. 8.5. References
9 Measuring Labor Market Transition Probabilities in Europe with Evidence from the EU-SILC
1. 9.1. Introduction
2. 9.2. Data, methods and limitations
3. 9.3. Results
4. 9.4. Conclusions
5. 9.5. References

PART 3: Student Assessment and Employment in Europe

10 Almost Graduated, Close to Employment? Taking into Account the Characteristics of Companies Recruiting at a University Job Placement Office
1. 10.1. Introduction
2. 10.2. Recruiters and graduates seeking an HEI common ground.
3. 10.3. Web survey pitfalls: considerations for data collection.
4. 10.4. Sampled recruiters: an outline
5. 10.5. Conclusion
6. 10.6. References
11 How Variation of Scores of the Programme for International Student Assessment can be Explained through Analysis of Information
1. 11.1. Introduction
2. 11.2. Multiplicative models and Zighera’s parameterization.
3. 11.3. Application to PISA surveys
4. 11.4. References

PART 4: Visualization

12 A Topological Discriminant Analysis
1. 12.1. Introduction
2. 12.2. Topological equivalence
3. 12.3. Topological discriminant analysis
4. 12.4. Application example
5. 12.5. Conclusion and perspectives
6. 12.6. Appendix
7. 12.7. References
13 Using Graph Partitioning to Calculate PageRank in a Changing Network
1. 13.1. Introduction
2. 13.2. Changes in personalization vector
3. 13.3. Adding or removing edges between components
4. 13.4. Conclusions
5. 13.5. References
14 Visualizing the Political Spectrum of Germany by Contiguously Ordering the Party Policy Profiles
1. 14.1. Introduction
2. 14.2. The model
3. 14.3. Conclusions
4. 14.4. References

List of Authors

Index

End User License Agreement

List of Tables

Introduction

Table I.1. Various cases of the maximum association principle
Table I.2. An example of interval data

Chapter 1

Table 1.1. Values of likelihood ratio test G² of HMMM associated with CG models
Table 1.2. Values of likelihood ratio G² test of HMMM
Table 1.3. Second-order marginal log-linear parameters

Chapter 2

Table 2.1. Description of innovation models

Chapter 3

Table 3.1. Data for IF and of explanatory variables included in the study
Table 3.2. Normality test for variables H-J, H-Ed and AnsP
Table 3.3. ANOVA and Levene’s tests among terciles for H-J, H-Ed and AnsP
Table 3.4. LSD test associated with ANOVA of terciles for H-J, H-Ed and AnsP
Table 3.5. Estimated coefficients of the preliminary logit regession
Table 3.6. Estimated coefficients of the final logit regession
Table 3.7. CCR provided by the logit model (cut-point: 0.5)

Chapter 4

Table 4.1. ARIMA–GARCH filters
Table 4.2. Pearson’s (left) and Kendall’s (right) correlation coefficients for t...
Table 4.3. The summary of the best 4-dimensional Vine copula

Chapter 5

Table 5.1. Dissimilarity measurement for individual countries
Table 5.2. Dissimilarity measurement for grouped countries
Table 5.3. Kyoto Annex I countries’ quantified emissions targets. Countries with...

Chapter 6

Table 6.1. Sample five-star rating data
Table 6.2. Sample five-star rating data statistics and probabilities

Chapter 7

Table 7.1. Descriptions of the seven items
Table 7.2. The measure of the seven items (period 2008–2013)
Table 7.3. Average mean of Rasch standardized scores for sociodemographic charac...
Table 7.4. The measure of the seven items (period 2013–2015)
Table 7.5. Average mean for Rasch standardized scores for sociodemographic chara...
Table 7.6. Contingency table of the levels of difficulty for the 2008–2012 perio...
Table 7.7. Description of the groups

Chapter 8

Table 8.1. Input probabilities and transition probability matrices, Greece, 2006...
Table 8.2. Prais–Shorrock, immobility, Bartholomew and Prais–Bibby indices, Gree...
Table 8.3. Input probabilities and transition probability matrices, Italy, 2006–...
Table 8.4. Prais–Shorrock, immobility, Bartholomew and Prais–Bibby indices, Ital...
Table 8.5. Input probabilities and transition probability matrices, Portugal, 20...
Table 8.6. Prais–Shorrock, immobility, Bartholomew and Prais–Bibby indices, Port...
Table 8.7. Input probabilities and transition probability matrices, Spain, 2006–...
Table 8.8. Prais–Shorrock, immobility, Bartholomew and Prais–Bibby indices, Spai...

Chapter 9

Table 9.1. Probability of remaining full-time employed, part-time employed, unem...
Table 9.2. Transition probability matrices, EU-SILC, 2006–2013, first group
Table 9.3. Transition probability matrices, EU-SILC, 2006–2013, second group
Table 9.4. Transition probability matrices, EU-SILC, 2006–2013, third group
Table 9.5. Input probabilities to full-time/part-time employment, unemployment a...
Table 9.6. Mobility indices using the EU-SILC data sets, 2006–2013

Chapter 10

Table 10.1. Return rate and specification of web survey results by response type
Table 10.2. Return rate from CAWI
Table 10.3. Sector of the company registering at Almalaurea for recruitment at t...
Table 10.4. Overall ranking of candidates’ characteristics for the five job posi...

Chapter 11

Table 11.1. Average scores in mathematics of French and German pupils by gender ...
Table 11.2. Order 1 (up) and 2 (down) parameters of model [11.4] for France
Table 11.3. Order 1 (up) and 2 (down) parameters of model [11.5] for Germany

Chapter 12

Table 12.1. Data set
Table 12.2. Centers of gravity matrix in ℝ⁴
Table 12.3. Matrix of cosine measure between the centers of gravity
Table 12.4. Confusion matrix – topological discriminant analysis
Table 12.5. Confusion matrix – linear discriminant analysis
Table 12.6. Confusion matrix – multinomial logistic regression
Table 12.7. Some proximity measures for continuous data
Table 12.8. Main results of the TDA according to different proximity measures

Chapter 14

Table 14.1. Party positions on policy questions

List of Illustrations

Chapter 1

Figure 1.1. SCGM with the labeled arc X₃ – X₄ referring to modality of X₁ and ...
Figure 1.2. CG models
Figure 1.3. SCG model C4

Chapter 2

Figure 2.1. Triple helix model
Figure 2.2. Outputs of innovation per Million GDP (USD)

Chapter 3

Figure 3.1. 95% LSD confidence intervals

Chapter 4

Figure 4.1. Vine tree of the construction [4.1] having the second variable as a ...
Figure 4.2. C-vine tree (left) and D-vine tree (right)
Figure 4.3. Seasonally and calendar-adjusted quarterly data for the V4 countries...
Figure 4.4. Residuals transformed to the unit interval
Figure 4.5. Partial correlation coefficients for the residuals
Figure 4.6. The tree structure of the optimal Vine copula with pair copula famil...
Figure 4.7. Contour plots of the optimal Vine copula (1=Cz, 2=Hu, 3=Pl, 4=Sk) an...

Chapter 5

Figure 5.1. Comparing methods for inequality measurement of Annex I countries, 2...
Figure 5.2. Between and within group inequality measurement of Annex I countries...

Chapter 6

Figure 6.1. Normalized entropy of ratings k = 1. For a color version of the figu...
Figure 6.2. Normalized Entropy of Rating k=2. For a color version of the figure,...
Figure 6.3. Normalized entropy of rating k=3. For a color version of the figure,...
Figure 6.4. Normalized entropy of rating k=4. For a color version of the figure,...
Figure 6.5. Entropy differences of max entropy distribution s.t. E(R) and Var(R)...
Figure 6.6. Maximum entropy of ratings for k=5, 6, 19, 29. For a color version o...
Figure 6.7. Normalized entropy differences of max entropy distribution s.t. E(R)...
Figure 6.8. Entropy of 1,000 five-star ratings data samples. For a color version...
Figure 6.9. False rate% of fitted binomial to 1,000 data sets of ratings. For a ...

Chapter 7

Figure 7.1. A representation of an item-map person
Figure 7.2. The distribution of the standardized measure for obtaining financing
Figure 7.3. The distribution of the standardized measure for obtaining financing
Figure 7.4. The regression tree of the indicators of SMEs’ difficulties in acces...

Chapter 8

Figure 8.1. School-to-work probabilities, Greece, LFS 2006–2013. For a color ver...
Figure 8.2. School-to-work probabilities, Italy, LFS 2006–2013. For a color vers...
Figure 8.3. School-to-work probabilities, Spain, LFS 2006–2013. For a color vers...
Figure 8.4. School-to-work probabilities, Portugal, LFS 2006–2013. For a color v...

Chapter 9

Figure 9.1. Full-time and part-time employment probabilities for new school leav...
Figure 9.2. Mobility indices, Group 1, EU-SILC, 2006–2013. For a color version o...
Figure 9.3. Mobility indices, Group 2, EU-SILC, 2006–2013. For a color version o...
Figure 9.4. Mobility indices, Group 3, EU-SILC, 2006–2013. For a color version o...

Chapter 12

Figure 12.1. Minimal spanning tree graph – adjacency matrix
Figure 12.2. Gabriel graph – adjacency matrix. For a color version of the figure...
Figure 12.3. Relative neighborhood graph – adjacency matrix. For a color version...
Figure 12.4. Gabriel graph – adjacency matrix. For a color version of the figure...

Chapter 13

Figure 13.1. Example of a graph and corresponding components from the SCC partit...

Chapter 14

Figure 14.1. Proximity triangle (correlations ρ_ij in %) for the parties ordered ...
Figure 14.2. Principal components analysis solution: eigenvector plot for the co...
Figure 14.3. Proximity triangle (correlations ρ_ij in %) for the parties ordered ...

First published 2019 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.

Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:

ISTE Ltd
27-37 St George’s
London SW19 4EU
UK
www.iste.co.uk

John Wiley & Sons, Inc.
Road 111 River Street
Hoboken, NJ 07030
USA
www.wiley.com

The rights of Christos H. Skiadas and James R. Bozeman to be identified as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988.

Library of Congress Control Number: 2018965157

British Library Cataloguing-in-Publication Data

A CIP record for this book is available from the British Library

ISBN 978-1-78630-447-6

Preface

Thanks to the significant work by the authors and contributors, we have developed this book, the second of two volumes. 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 analytic tools. Hence, the need for publications is evident. New works 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.

In this volume, we present the collected material in four parts, including 14 chapters, in a form that will provide the reader with theoretical and applied information on data analysis methods, models and techniques along with appropriate applications. The results of the work in these chapters are used for further study throughout Europe, including the Nordic countries, the V4 states, southern Europe, Germany and the United Kingdom. Other topics include computing, entropy, innovation and quality assurance.

Before the chapters, we include an excellent introductory and review paper titled “50 Years of Data Analysis: From Exploratory Data Analysis to Predictive Modeling and Machine Learning” by Gilbert Saporta, a leading expert in the field. The paper was based on the speech given for the celebration of his 70th birthday at the ASMDA2017 International Conference in London (held in De Morgan House of the London Mathematical Society).

The current volume contains the following four parts:

Part 1, Applications, includes six chapters: “Context-specific Independence in Innovation Studies” by Federica Nicolussi and Manuela Cazzaro; “Analysis of the Determinants and Outputs of Innovation in the Nordic Countries” by Catia Rosario, Antonio Augusto Costa and Ana Lorga da Silva; “Bibliometric Variables Determining the Quality of a Dentistry Journal” by Pilar Valderrama, Manuel Escabias, Evaristo Jiménez-Contreras, Mariano J. Valderrama and Pilar Baca; “Analysis of Dependence among Growth Rates of GDP of V4 Countries Using Four-dimensional Vine Copulas” by Jozef Komornik, Magda Komornikova and Tomas Bacigal; “Monitoring the Compliance of Countries on Emissions Mitigation Using Dissimilarity Indices” by Eleni Ketzaki, Stavros Rallakis, Nikolaos Farmakis and Eftichios Sartzetakis; and “Maximum Entropy and Distributions of Five-Star Ratings” by Yiannis Dimotikalis.

Part 2, The Impact of the Economic and Financial Crisis in Europe, contains one chapter about credit: “Access to Credit for SMEs after the 2008 Financial Crisis: The Northern Italian Perspective” by Cinzia Colapinto and Mariangela Zenga. This is followed by two chapters on the labor market: “Gender-Based Differences in the Impact of the Economic Crisis on Labor Market Flows in Southern Europe”, and “Measuring Labor Market Transition Probabilities in Europe with Evidence from the EU-SILC, both by Maria Symeonaki, Maria Karamessini and Glykeria Stamatopoulou.

Part 3, Student Assessment and Employment in Europe, has an article concerning university students who are about to graduate and hence are close to employment that is related to Part 2: “Almost Graduated, Close to Employment? Taking into Account the Characteristics of Companies Recruiting at a University Job Placement Office” by Franca Crippa, Mariangela Zenga and Paolo Mariani, followed by a paper on how students are assessed: “How Variation of Scores of the Programme for International Student Assessment Can be Explained through Analysis of Information” by Valérie Girardin, Justine Lequesne and Olivier Thévenon.

Part 4, Visualization, examines this topic in computing: “A Topological Discriminant Analysis” by Rafik Abdesselam, followed by “Using Graph Partitioning to Calculate PageRank in a Changing Network” by Christopher Engström and Sergei Silvestrov, and in politics: “Visualizing the Political Spectrum of Germany by Contiguously Ordering the Party Policy Profiles by Andranik Tangian.

We would like to thank the authors of and contributors to this book. We pass on our sincere appreciation to the referees for their hard work and dedication in providing an improved book form. Finally, we express our thanks to the secretariat and, of course, the publishers.

December 2018
Christos H. SKIADAS, Athens, Greece
James R. BOZEMAN, Bormla, Malta

Data Analysis and Applications 2

Utilization of Results in Europe and Other Topics

Preface