Gini Coefficient or Gini Index was originally defined as a standardized measure of statistical dispersion intended to understand an income distribution. It has evolved into quantifying inequity in all kinds of distributions of wealth, gender parity, access to education and health services, environmental policies, and numerous other attributes of importance. Gini Inequality Index: Methods and Applications features original high-quality peer-reviewed chapters prepared by internationally acclaimed researchers. They provide innovative methodologies whether quantitative or qualitative, covering welfare economics, development economics, optimization/non-optimization, econometrics, air quality, statistical learning, inference, sample size determination, big data science, and some heuristics.
The importance of this effort lies on the fact that never before has such a wide dimension of leading research inspired by Gini's works and their applicability been collected in one edited volume. The volume also showcases modern approaches to the research of a number of very talented and upcoming younger contributors and collaborators. This feature will give readers a window with a distinct view of what emerging research in this field may entail in the near future.
Table of Contents
1. Introducing Informal Inequality Measures (IIMs) Constructed from U-statistics of Degree Three or Higher in Analyzing Economic Disparity. 2. The Decomposition of the Gini Index Between and Within Groups: A Key Factor in Gender Studies. 3. A Note on the Decomposition of Health Inequality by Population Subgroups in the Case of Ordinal Variables. 4. The Gini index decomposition and the overlapping between population subgroups. 5. Gini’s Mean Difference Based Minimum Risk Point Estimator of Mean. 6. The Gini concentration index for the study of survival. 7. An Axiomatic Analysis of Air Quality Assessment. 8. Sequential Interval and Point Estimation of Gini Index by Controlling Accuracies Relative to the Mean. 9. A Test on Correlation based on Gini's Mean Difference. 10. Multi-group Segregation for Nominal and Ordinal Categorical Data. 11. Exploring Fixed-Accuracy Estimation for Population Gini Inequality Index Under Big Data: A Passage to Practical Distribution-Free Strategies.
Professor Nitis Mukhopadhyay received PhD degree from Indian Statistical Institute-Calcutta based on a dissertation dated 1975. He is a full professor (since 1985), Department of Statistics, University of Connecticut-Storrs and served as Head of this department during 1987-90. Professor Mukhopadhyay is an Honorary Fellow of the Institute of Applied Statistics Sri Lanka. He is a world-traveler visiting many corners of the globe as an international delegate delivering specially invited Plenary, Keynote, Opening and other major presentations and run workshops on topics including Statistics, Applied Probability, Mathematics, Management Informatics, Econometrics and Teaching.
Professor Partha Pratim Sengupta is Professor of Economics, Ex-Head of Department of Humanities and Social Sciences, and Founder Head of Department of Management Studies, National Institute of Technology Durgapur, India. He has teaching experiences of nearly 38 years at UG and PG level and research experience of 24 years. Professor Sengupta obtained the Ph.D. degree in Economics from Jadavpur University, India. To date, twenty one students have been awarded Doctorate degree under his supervision. He has published more than one hundred research papers in reputed national and international level peer reviewed and indexed journals.