Data analysis and applications. 2, Utilization of results in Europe and other topics / edited by Christos H. Skiadas, James R. Bozeman.

Includes bibliographical references and index.

Online resource; title from digital title page (viewed on March 15, 2019).

Cover; Half-Title Page; Title Page; Copyright Page; Contents; Preface; Introduction: 50 Years of Data Analysis: From Exploratory Data Analysis to Predictive Modeling and Machine Learning; I.1. The revolt against mathematical statistics; I.2. EDA and unsupervised methods for dimension reduction; I.2.1. The time of syntheses; I.2.2. The time of clusterwise methods; I.2.3. Extensions to new types of data; I.2.4. Nonlinear data analysis; I.2.5. The time of sparse methods; I.3. Predictive modeling; I.3.1. Paradigms and paradoxes; I.3.2. From statistical learning theory to empirical validation

I.3.3. ChallengesI. 4. Conclusion; I.5. References; PART 1: Applications; 1. Context-specific Independence in Innovation Study; 1.1. Introduction; 1.2. Parametrization for CS independencies; 1.3. Stratified chain graph models; 1.4. Application on real data; 1.5. Conclusion; 1.6. References; 2. Analysis of the Determinants and Outputs of Innovation in the Nordic Countries; 2.1. Introduction; 2.2. Innovation; 2.3. Methodology; 2.4. Results; 2.5. Conclusion; 2.6. References; 3. Bibliometric Variables Determining the Quality of a Dentistry Journal; 3.1. Introduction; 3.2. Statistical methodology

3.3. Results3.4. Conclusions; 3.5. Acknowledgment; 3.6. References; 4. Analysis of Dependence among Growth Rates of GDP of V4 Countries Using Four-dimensional Vine Copulas; 4.1. Introduction; 4.2. Theory; 4.3. Results; 4.4. Conclusion and future work; 4.5. Acknowledgment; 4.6. References; 5. Monitoring the Compliance of Countries on Emissions Mitigation Using Dissimilarity Indices; 5.1. Introduction; 5.2. The proposed method; 5.2.1. Description of method for individual data; 5.2.2. Description of method for grouped data; 5.3. Application of method

5.3.1. Application of method for individual data5.3.2. Application of method for grouped data; 5.4. Conclusions; 5.5. Appendix; 5.6. References; 6. Maximum Entropy and Distributions of Five-Star Ratings; 6.1. Introduction; 6.2. Entropy framework to five-star ratings; 6.3. Maximum entropy of ratings for values k = 1,2,3,. . .,30; 6.3.1. Ratings with two outcomes (k = 1); 6.3.2. Ratings with three Outcomes (k=2); 6.3.3. Ratings with four outcomes (k=3); 6.3.4. Ratings with five outcomes (k = 4); 6.3.5. Rating entropy for outcomes k>4

6.3.6. Maximum entropy constraints for the binomial distribution6.4. Application to real five-star rating data; 6.5. Conclusions; 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; 7.1. Introduction; 7.2. Italian SMEs and access to credit; 7.3. The data; 7.4. Methodology; 7.5. Analysis and discussion; 7.5.1. The measure for the Great Recession period (2008-2012); 7.5.2. The measure for the recovery period (2013-2015); 7.5.3. Comparing the two crisis phases