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230102 ||| eng |
| 020 |
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|a 9781394165544
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| 020 |
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|a 1394165528
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|a 1394165544
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|a 9781394165520
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| 050 |
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4 |
|a QA76.9.Q36
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| 100 |
1 |
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|a Zafeiris, Konstantinos N.
|e editor
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| 245 |
0 |
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|a Data analysis and related applications, 2: Multivariate, health and demographic data analysis
|c edited by Konstantinos N. Zafeiris, [and four others]
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| 260 |
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|a London
|b ISTE Ltd
|c 2022
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| 300 |
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|a 444 pages
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| 505 |
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|a 6.3.1. Setting parameter -- 6.3.2. Relationship between the American option price and economic situation i -- 6.3.3. Relationship between the American option price and the asset price s -- 6.3.4. Relationship between the American option price and maturity T -- 6.3.5. Relationship between the American option price and transition probabilities P -- 6.3.6. Consideration of the optimal exercise region -- 6.4. Conclusion -- 6.5. References -- 7. The Implementation of Hierarchical Classifications and Cochran's Rule in the Analysis of Social Data -- 7.1. Introduction -- 7.2. Methods -- 7.3. Results
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| 505 |
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|a Includes bibliographical references and index
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|a 4. Capturing School-to-Work Transitions Using Data from the First European Graduate Survey -- 4.1. Introduction -- 4.2. Data and methodology -- 4.3. Results -- 4.4. Conclusion -- 4.5. References -- 5. A Cluster Analysis Approach for Identifying Precarious Workers -- 5.1. Introduction -- 5.2. Data and methodology -- 5.3. Results -- 5.4. Conclusion and discussion -- 5.4.1. Declarations -- 5.5. References -- 6. American Option Pricing Under a Varying Economic Situation Using Semi-Markov Decision Process -- 6.1. Introduction -- 6.2. American option pricing -- 6.3. Exercising strategies
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|a Cover -- Half-Title Page -- Title Page -- Copyright Page -- Contents -- Preface -- PART 1 -- 1. A Topological Clustering of Variables -- 1.1. Introduction -- 1.2. Topological context -- 1.2.1. Reference adjacency matrices -- 1.2.2. Quantitative variables -- 1.2.3. Qualitative variables -- 1.2.4. Mixed variables -- 1.3. Topological clustering of variables -- selective review -- 1.4. Illustration on real data of simple examples -- 1.4.1. Case of a set of quantitative variables -- 1.4.2. Case of a set of qualitative variables -- 1.4.3. Case of a set of mixed variables -- 1.5. Conclusion
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|a 1.6. Appendix -- 1.7. References -- 2. A New Regression Model for Count Compositions -- 2.1. Introduction -- 2.1.1. Distributions for count vectors -- 2.2. Regression models and Bayesian inference -- 2.3. Simulation studies -- 2.3.1. Fitting study -- 2.3.2. Excess of zeroes -- 2.4. Application to real electoral data -- 2.5. References -- 3. Intergenerational Class Mobility in Greece with Evidence from EU-SILC -- 3.1. Introduction -- 3.2. Data and methods -- 3.3. The trends of class mobility between different birth cohorts -- 3.4. Conclusion -- 3.5. References
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|a 7.4. Conclusion -- 7.5. References -- 8. Dynamic Optimization with Tempered Stable Subordinators for Modeling River Hydraulics -- 8.1. Introduction -- 8.2. Mathematical model -- 8.3. Optimization problem -- 8.4. HJBI equation: formulation and solution -- 8.5. Concluding remarks -- 8.6. Acknowledgments -- 8.7. References -- PART 2 -- 9. Predicting Event Counts in Event-Driven Clinical Trials Accounting for Cure and Ongoing Recruitment -- 9.1. Introduction -- 9.2. Modeling the process of event occurrence -- 9.2.1. Estimating parameters of the model
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| 653 |
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|a Quantitative research / fast
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| 653 |
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|a Quantitative research / http://id.loc.gov/authorities/subjects/sh2007000909
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| 653 |
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|a Recherche quantitative
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| 700 |
1 |
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|a Skiadas, Christos H.
|e editor
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| 700 |
1 |
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|a Dimotikalis, Yiannis
|e editor
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| 700 |
1 |
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|a Karagrigoriou, Alex
|e editor
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 490 |
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|a Big data, artificial intelligence and data analysis set
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| 500 |
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|a 9.3. Predicting event counts for patients at risk
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| 024 |
8 |
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|a 10.1002/9781394165544
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| 776 |
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|z 9781394165520
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| 776 |
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|z 1394165528
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| 776 |
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|z 1394165544
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| 776 |
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|z 9781786307729
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| 776 |
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|z 9781394165544
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| 856 |
4 |
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|u https://learning.oreilly.com/library/view/~/9781786307729/?ar
|x Verlag
|3 Volltext
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| 082 |
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|a 001.42
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| 082 |
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|a 001.42
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| 520 |
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|a The scientific field of data analysis is constantly expanding due to the rapid growth of the computer industry and the wide applicability of computational and algorithmic techniques, in conjunction with new advances in statistical, stochastic and analytic tools. There is a constant need for new, high-quality publications to cover the recent advances in all fields of science and engineering. This book is a collective work by a number of leading scientists, computer experts, analysts, engineers, mathematicians, probabilists and statisticians who have been working at the forefront of data analysis and related applications. The chapters of this collaborative work represent a cross-section of current concerns, developments and research interests in the above scientific areas. The collected material has been divided into appropriate sections to provide the reader with both theoretical and applied information on data analysis methods, models and techniques, along with related applications
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