This book compares the population history of Japan, Korea, Taiwan, and China to understand such emergent changes as extremely low fertility in Korea and Taiwan, compressed urbanization and a massive diaspora from Korea, early population aging relative to economic development in China, and changing patterns of cross-border migration in the region. After discussing the origin of each ethnic group, premodern population changes are examined by reviewing historical demographic studies including those written in local languages. A new population estimation for premodern Korea is also presented.

This book based on static indicators and dynamic big data from local electric vehicles, is the first New-Energy Vehicles (NEVs) research report on the Big Data in China. Using the real-time big data collected by China's National Monitoring and Management Platform for NEVs, this book delves into the main annual technological progress of NEVs, the vehicle operating characteristics, it also anticipates the trend of NEVs industry.

The ten-volume set LNCS 12949 – 12958 constitutes the proceedings of the 21st International Conference on Computational Science and Its Applications, ICCSA 2021, which was held in Cagliari, Italy, during September 13 – 16, 2021. The event was organized in a hybrid mode due to the Covid-19 pandemic.The 466 full and 18 short papers presented in these proceedings were carefully reviewed and selected from 1588 submissions. The books cover such topics as multicore architectures, blockchain, mobile and wireless security, sensor networks, open source software, collaborative and social computing systems and tools, cryptography, applied mathematics human computer interaction, software design engineering, and others. Part IV of the set includes the papers on Urban and Regional Planning and the proceedings of the following workshops: ​International Workshop on Blockchain and Distributed Ledgers: Technologies and Applications (BDLTA 2021); International Workshop on Computational and Applied Mathematics (CAM 2021); International Workshop on Computational and Applied Statistics (CAS 2021); International Workshop on Computerized Evaluation of Economic Activities: Urban Spaces (CEEA 2021).

This handbook is a realization of a long term goal of BMDP Statistical Software. As the software supporting statistical analysis has grown in breadth and depth to the point where it can serve many of the needs of accomplished statisticians it can also serve as an essential support to those needing to expand their knowledge of statistical applications. Statisticians should not be handicapped by heavy computation or by the lack of needed options. When Applied Statistics, Principle and Examples by Cox and Snell appeared we at BMDP were impressed with the scope of the applications discussed and felt that many statisticians eager to expand their capabilities in handling such problems could profit from having the solutions carried further, to get them started and guided to a more advanced level in problem solving. Who would be better to undertake that task than the authors of Applied Statistics? A year or two later discussions with David Cox and Joyce Snell at Imperial College indicated that a wedding of the problem statements and suggested solutions with control language to accomplish these analyses would further the learning process for many statisticians. They were willing to undertake the project. Joyce Snell has done an excellent job of melding the two approaches and has carried many of the problems a step further by suggesting alternate approaches and follow-up analyses.

Rapid advances in modelling research have created new challenges and opportunities for statisticians. Statistical inference in observational studies and many other emerging fields have motivated statisticians worldwide to develop cutting-edge methods and analytical strategies.

The book offers an introduction to the technical foundation of decentralized insurance models, for advanced undergraduate students, graduate students and practitioners. The book is self-contained and anyone with a basic knowledge of probability and statistics should be able to follow through the entire book. It adopts a minimalist approach to describe the essential elements and first principles so that readers can get a gist of these models without being overwhelmed with too much technicality. It can be used as a reference for business model designs. The inclusion of exercises and practical examples makes the book suitable for advanced courses on decentralized insurance and risk sharing.

This book presents recent developments in multivariate and robust statistical methods. Featuring contributions by leading experts in the field it covers various topics, including multivariate and high-dimensional methods, time series, graphical models, robust estimation, supervised learning and normal extremes. It will appeal to statistics and data science researchers, PhD students and practitioners who are interested in modern multivariate and robust statistics. The book is dedicated to David E. Tyler on the occasion of his pending retirement and also includes a review contribution on the popular Tyler’s shape matrix.

This book provides state-of-the-art theory and application in geostatistics.
Geostatistics Toronto 2021 includes 28 short abstracts, 18 extended abstracts, and 7 full articles in the fields of geostatistical theory, multi-point statistics, earth sciences, mining, optimal drilling, domains, seismic, classification uncertainty risk, and artificial intelligence and machine learning. All contributions were presented at the 11th International Geostatistics Congress held in virtually at Toronto, Canada, from July 12-16, 2021.

This book presents the latest findings on statistical inference in multivariate, multilinear and mixed linear models, providing a holistic presentation of the subject. It contains pioneering and carefully selected review contributions by experts in the field and guides the reader through topics related to estimation and testing of multivariate and mixed linear model parameters. Starting with the theory of multivariate distributions, covering identification and testing of covariance structures and means under various multivariate models, it goes on to discuss estimation in mixed linear models and their transformations.

This book provides a complete introduction into spatial networks. It offers the mathematical tools needed to characterize these structures and how they evolve in time and presents the most important models of spatial networks.

This book discusses recent theoretical developments in agglomerative hierarchical clustering. The general understanding of agglomerative hierarchical clustering is that its theory was completed long ago and there is no room for further methodological studies, at least in its fundamental structure. This book has been planned counter to that view: it will show that there are possibilities for further theoretical studies and they will be not only for methodological interests but also for usefulness in real applications. When compared with traditional textbooks, the present book has several notable features. First, standard linkage methods and agglomerative procedure are described by a general algorithm in which dendrogram output is expressed by a recursive subprogram.

This is the first book to show the capabilities of Microsoft Excel to teach educational and psychological statistics effectively. It is a step-by-step exercise-driven guide for students and practitioners who need to master Excel to solve practical problems in education and psychology. If understanding statistics isn’t your strongest suit, you are not especially mathematically-inclined, or if you are wary of computers, this is the right book for you.

This book provides the statistical basis for quantitative risk management by presenting and explaining the most important distributions. Distributions describe the occurrence and impact of a risk. They are a prerequisite for risk aggregation, risk analysis and risk assessment as required by the German revision standards IDW PS 340, StaRUG and FISG.

This textbook's significant contribution is the unified derivation of data-assimilation techniques from a common fundamental and optimal starting point, namely Bayes' theorem. Unique for this book is the "top-down" derivation of the assimilation methods. It starts from Bayes theorem and gradually introduces the assumptions and approximations needed to arrive at today's popular data-assimilation methods.

This book provides a complete introduction into spatial networks. It offers the mathematical tools needed to characterize these structures and how they evolve in time and presents the most important models of spatial networks.