The Handbook of Computational Statistics – Concepts and Methods (second edition) is a revision of the first edition published in 2004, and contains additional comments and updated information on the existing chapters, as well as three new chapters addressing recent work in the field of computational statistics. This new edition is divided into 4 parts in the same way as the first edition. It begins with ‘How Computational Statistics became the backbone of modern data science’ (Ch.1): an overview of the field of Computational Statistics, how it emerged as a separate discipline, and how its own development mirrored that of hardware and software, including a discussion of current active research. The second part (Chs. 2 – 15) presents several topics in the supporting field of statistical computing. Emphasis is placed on the need for fast and accurate numerical algorithms, and some of the basic methodologies for transformation, database handling, high-dimensional data and graphics treatment are discussed. The third part (Chs. 16 – 33) focuses on statistical methodology. Special attention is given to smoothing, iterative procedures, simulation and visualization of multivariate data. Lastly, a set of selected applications (Chs. 34 – 38) like Bioinformatics, Medical Imaging, Finance, Econometrics and Network Intrusion Detection highlight the usefulness of computational statistics in real-world applications.
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Part I Computational Statistics.- Part II Statistical Computing.- Part III Statistical Methodology.- Part IV Selected Applications
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James E. Gentle is a Professor of Computational Statistics at George Mason University. His research interests include Monte Carlo methods and computational finance. He is an elected member of the ISI and a Fellow of the American Statistical Association.
Wolfgang Karl Härdle is a Professor of Statistics at the Humboldt-Universität zu Berlin and the Director of CASE – the Centre for Applied Statistics and Economics. He teaches quantitative finance and semi-parametric statistical methods. His research focuses on dynamic factor models, multivariate statistics in finance and computational statistics. He is an elected member of the ISI and an advisor to the Guanghua School of Management, Peking University and to National Central University, Taiwan.
Yuichi Mori is a Professor of Statistics and Informatics at Okayama University of Science. His research interests include efficient computing in multivariate methods, dimension reduction and variable selection, and statistics education. He is an elected member of the ISI and served as a council member of the IASC from 2003 to 2007.
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