Editors: Krishna Kumar Mohbey, Arvind Pandey, Dharmendra Singh Rajput

Predictive Analytics Using Statistics and Big Data: Concepts and Modeling

eBook: US $39 Special Offer (PDF + Printed Copy): US $47
Printed Copy: US $66
Library License: US $156
ISBN: 978-981-14-9051-4 (Print)
ISBN: 978-981-14-9049-1 (Online)
Year of Publication: 2020
DOI: 10.2174/97898114904911200101


This book presents a selection of the latest and representative developments in predictive analytics using big data technologies. It focuses on some critical aspects of big data and machine learning and provides studies for readers. The chapters address a comprehensive range of advanced data technologies used for statistical modeling towards predictive analytics.

Topics included in this book include:

- Categorized machine learning algorithms

- Player monopoly in cricket teams.

- Chain type estimators

- Log type estimators

- Bivariate survival data using shared inverse Gaussian frailty models

- Weblog analysis

- COVID-19 epidemiology

This reference book will be of significant benefit to the predictive analytics community as a useful guide of the latest research in this emerging field.


Predictive analytics is the art and science of proposed predictive systems and models. With tuning over time, these models can predict an outcome with a far higher statistical probability than mere guesswork. Predictive analytics plays an essential role in the digital era. Most of the business strategies and planning depend on prediction and analytics using statistical approaches. With the increasing digitization day by day, analytical challenges are also increasing at the same rate—digital information, which is rapidly growing, generating vast amounts of data. Hence, the design of computing, storage infrastructures, and algorithms needed to handle these "big data" problems. Big Data is collecting and analyzing complex data in terms of volume, variety, and velocity. The most extensive selection of big data is from digital information, social media, IoT, sensor, etc.

Predictive analytics can be done with the help of various big data technologies and statistical approaches. Big data technologies include Hadoop, Hive, HBase, and Spark. There are numerous statistical approaches to perform predictive analytics, including Bayesian analysis, Sequential analysis, Statistical prediction, risk prediction, and decision analytics.

This book presents some latest and representative developments in predictive analytics using big data technologies. It focuses on some critical aspects of big data and machine learning and provides descriptions for these technologies. The book consists of seven chapters. Chapter 1 discusses data analytics in multiple fields with machine learning algorithms. An application of bootstrap sampling is presented in chapter 2 with the case study of quantifying player's monopoly in a cricket team. Successive sampling for mean estimation is discussed in chapter 3. Chapter 4 discussed log type estimators of population mean under ranked set sampling. Bivariate survival data analysis is represented in chapter 5. An approach for weblog data analysis using machine learning techniques is discussed in chapter 6. Chapter 7 discussed an epidemic analysis of COVID-19 using exploratory data analysis approaches.

Many eminent colleagues made a significant impact on the development of this eBook. First, we would like to thank all the authors for their exceptional contributions to the eBook and their patience for the long process of editing this eBook. We would also like to thank the reviewers for their insightful and valuable feedback and comments that improved the book's overall quality.

Krishna Kumar Mohbey
Central University of Rajasthan

Arvind Pandey
Central University of Rajasthan


Dharmendra Singh Rajput
VIT Vellore


.An Introduction to Sobolev Spaces.
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