Statistics in Industry and Government (Volume 53) (Handbook of Statistics, Volume 53). This item is unavailable.Statistics in Industry and Government (Volume 53) (Handbook of Statistics, Volume 53)

• Provides an easy to understand accounting of valuable statistics concepts
• Coverage helps readers easily implement tactics discussed
• Written by experts in the field who comment on the kinds of benefits both industry and governments can gain using statistical data

Ravindra Khattree is a distinguished professor of statistics at Oakland University, U.S.A. and a co-director of the Center for Data Science and Big Data Analytics at the same university. In 1985, he earned a doctorate from the University of Pittsburgh with Calyampudi Radhakrishna Rao as his advisor. His contribution to the Fountain–Khattree–Peddada Theorem in Pitman measure of closeness is one of the important results of his work. Khattree is the coauthor of two books and has coedited two volumes. He has served as an associate editor of the Communications in Statistics journal. He was Chief editor of Journal of Statistics and Applications for more than ten years. He is an elected fellow of the American Statistical Association. In 2002, Khattree received the Young Researcher Award from the International Indian Statistical Association. Khattree was honored with a fellowship in the American Statistical Association in 2003 and became an elected member of the International Statistical Institute in 2004. He is also a recipient of Oakland University Research Excellence Award (2008).

Dr. Hon Yiu So is an Assistant Professor in the Department of Mathematics and Statistics at Oakland University. He received his Ph.D. in Mathematics with a concentration in Statistics from McMaster University in 2016. Dr. So's research lies at the intersection of reliability analysis, survey sampling, machine learning, and missing data methodologies. He has authored or co-authored over 25 peer-reviewed journal articles and book chapters in these fields. He is the co-author of the monograph Accelerated Life Testing of One-Shot Devices: Data Collection and Analysis. His work has contributed to both theoretical advancement and practical application in statistical science, particularly in the context of reliability engineering and public health research.

Arni S.R. Srinivasa Rao works in pure mathematics, applied mathematics, probability, artificial intelligence and applications in medicine. He developed the concept of “Exact Deep Learning Machines”, which can provide designs for accurate predictions without any uncertainty. He had edited these handbooks jointly with renowned statistician Dr. C. R. Rao. He is a Professor at the Medical College of Georgia, Augusta University, U.S.A., and the Director of the Laboratory for Theory and Mathematical Modeling housed within the Division of Infectious Diseases, Medical College of Georgia, Augusta, U.S.A. Previously, Dr. Rao conducted research and/or taught at the Mathematical Institute, University of Oxford (2003, 2005-07), Indian Statistical Institute (1998-2002, 2006-2012), Indian Institute of Science (2002-04), University of Guelph (2004-06). Until 2012, Dr. Rao held a permanent faculty position at the Indian Statistical Institute. He has won the Heiwa-Nakajima Award (Japan) and Fast Track Young Scientists Fellowship in Mathematical Sciences (DST, New Delhi). Dr. Rao also proved a major theorem in stationary population models, such as, Rao’s Partition Theorem inPopulations, Rao-Carey Theorem in stationary populations, and developed mathematical modeling-based policies for the spread of diseases like HIV, H5N1, COVID-19, etc. He developed a new set of network models for understanding avian pathogen biology on grid graphs (these were called chicken walk models), AI Models for COVID-19, and received wide coverage in the science media. Dr. Rao is an elected Fellow of ISMMACS (Indian Society for Mathematical Modeling and Computer Simulation), and ISPS (Indian Society for Probability and Statistics). He developed concepts such as “Multilevel Contours within a bundle of Complex Number Planes”.