Data Science: Theory and Applications
Elsevier (Publisher)
Published on 12. February 2021
348 pages
978-0-323-85201-2 (ISBN)
Description
Data Science: Theory and Applications, Volume 44 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of interesting topics, including Modeling extreme climatic events using the generalized extreme value distribution, Bayesian Methods in Data Science, Mathematical Modeling in Health Economic Evaluations, Data Science in Cancer Genomics, Blockchain Technology: Theory and Practice, Statistical outline of animal home ranges, an application of set estimation, Application of Data Handling Techniques to Predict Pavement Performance, Analysis of individual treatment effects for enhanced inferences in medicine, and more.
Additional sections cover Nonparametric Data Science: Testing Hypotheses in Large Complex Data, From Urban Mobility Problems to Data Science Solutions, and Data Structures and Artificial Intelligence Methods.
- Provides the authority and expertise of leading contributors from an international board of authors
- Presents the latest release in the Handbook of Statistics series
- Updated release includes the latest information on Data Science: Theory and Applications
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02/2021
North-Holland
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Content
Section I: Animal Models and Ecological Large Data Methods
1. Statistical outline of animal home ranges: An application of set estimationAmparo Bällo and José Enrique Chacón
2. Modeling extreme climatic events using the generalized extreme value (GEV) distributionDiana Rypkema and Shripad Tuljapurkar
Section II: Engineering Sciences Data
3. Blockchain technology: Theory and practiceSrikanth Cherukupally
4. Application of data handling techniques to predict pavement performanceSireesh Saride, Pranav R.T. Peddinti and B. Munwar Basha
Section III: Statistical Estimation Designs: fractional fields, biostatistics and non-parametrics
5. On the usefulness of lattice approximations for fractional Gaussian fieldsSomak Dutta and Debashis Mondal
6. Estimating individual-level average treatment effects: Challenges, modeling approaches, and practical applicationsVictor B. Talisa and Chung-Chou H. Chang
7. Nonparametric data science: Testing hypotheses in large complex dataSunil Mathur
Section IV: Network Models and COVID-19 modeling
8. Network models in epidemiologyTae Jin Lee, Masayuki Kakehashi and Arni S.R. Srinivasa Rao
9. Modeling and forecasting the spread of COVID-19 pandemic in India and significance of lockdown: A mathematical outlookBrijesh P. Singh
10. Mathematical modeling as a tool for policy decision making: Applications to the COVID-19 pandemicJ.Panovska-Griffiths, C.C. Kerr, W. Waites and R.M. Stuart
