Data Science: Theory and Applications

Erscheinungsjahr 2021
ISBN: 978-0-323-85200-5
Verlag: William Andrew Publishing

Buch, Englisch, 348 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 670 g

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.

Zielgruppe

<p>Undergraduates, graduates, academics and researchers in the field of Handbook of Statistics</p>

Fachgebiete

• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Mathematische Modellierung
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik

Inhaltsverzeichnis

Section I: Animal Models and Ecological Large Data Methods

1. Statistical outline of animal home ranges: An application of set estimation Amparo Ba?llo and José Enrique Chacón

2. Modeling extreme climatic events using the generalized extreme value (GEV) distribution Diana Rypkema and Shripad Tuljapurkar

Section II: Engineering Sciences Data

3. Blockchain technology: Theory and practice Srikanth Cherukupally

4. Application of data handling techniques to predict pavement performance Sireesh 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 fields Somak Dutta and Debashis Mondal

6. Estimating individual-level average treatment effects: Challenges, modeling approaches, and practical applications Victor B. Talisa and Chung-Chou H. Chang

7. Nonparametric data science: Testing hypotheses in large complex data Sunil Mathur

Section IV: Network Models and COVID-19 modeling

8. Network models in epidemiology Tae 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 outlook Brijesh P. Singh

10. Mathematical modeling as a tool for policy decision making: Applications to the COVID-19 pandemic J.Panovska-Griffiths, C.C. Kerr, W. Waites and R.M. Stuart