1070 pages, 236 illus
The Handbook of Computational Statistics - Concepts and Methods is divided into 4 parts. It begins with an overview of the field of Computational Statistics, how it emerged as a seperate discipline, how it developed along the development of hard- and software, including a discussion of current active research.
The second part presents several topics in the supporting field of statistical computing. Emphasis is placed on the need for fast and accurate numerical algorithms, and it discusses some of the basic methodologies for transformation, data base handling and graphics treatment.
The third part focuses on statistical methodology. Special attention is given to smoothing, iterative procedures, simulation and visualization of multivariate data.
Finally a set of selected applications like Bioinformatics, Medical Imaging, Finance and Network Intrusion Detection highlight the usefulness of computational statistics.
From the reviews: "I found this volume interesting and can recommend it to anyone with an interest in statistical computing; most of the chapters provide nice overviews of their topic areas, and they are interesting and largely well written." Journal of the American Statistical Association, December 2005 "This is a very fine book, and I am very happy to own a copy. ! Each book comes with the owner's special numerical code that allows the owner to access the e-book version. Each of the chapters has a huge reference list ! ." (Technometrics, Vol. 47 (3), August, 2005) "The articles in this handbook cover the important subareas of computational statistics and give some flavor of the wide range of applications. ! should be included in the library of any organization involved in any way with computational statistics. The editors and their authors deserve to be commended ! . Everyone concerned with computational statistics will want and need to consult this volume. ! will be a considerable asset in the work of many a researcher and student of statistics. A definitive contribution that provokes applause ! stimulating further studies." (Current Engineering Practice, Vol. 48, 2004/2005) "The book contains 35 expository articles on various topics from the field of computational statistics written by experts in those fields. Each of the contributions describes ! the basic ideas, fundamental concepts and current research topics in the relevant area, concluding with a long list of references for further reading. ! The book is accompanied by an ebook version which can be downloaded onto a local computer. It enables to directly use links to internet sources all over the world." (Wolfgang zu Castell, Zentralblatt MATH, Vol. 1066, 2005) "There are many extremely well-written chapters in this book; referencing is good, and even when topics are not covered in detail, there are references for further reading. Indexing is surprisingly good for a multi-author piece of work. I am therefore pleased to have a copy of this book." (Paul Hewson, Statistical Methods in Medical Research, Vol. 15, 2006) "The "Handbook of Computational Statistics" ! is a comprehensive account of a large variety of algorithms, methods, and concepts of computational statistics and their use in statistical methodology. ! This book is definitely an obligatory reference for all applied statisticians who develop and implement statistical methods. ... Therefore it should be regarded as a welcome addition to the reference shelves of operational researchers, especially for those who concentrate on methodological issues." (Ulrich Kusters, OR Spectrum, Issue 26, 2006) "The stated purpose of this book is to provide a survey of concepts and fundamentals of computational statistics. ! The editors have done reasonably well in breadth of coverage. ! It is a good idea to publish such a handbook. The subject has developed ! most chapters contain good references and there are many excellent chapters with very clear expositions of both theory and practice. I am sure that I shall use it as a reference book ! ." (Anders Brix, Journal of the Royal Statistical Society Series A: Statistics in Society, Vol. 168 (4), 2005) "This book is intended to give an overview of the concepts and fundamentals of computational statistics. ! The book provides the reader with background information as well as tools and suggestions for research and references for further reading. ! To summarize, this book gives a unified framework to the somewhat fragmented area of computational statistics, with the separate chapters providing up-to-date discussions on important areas of the field. It is very useful handbook for all statisticians interested in applying computational statistics methods." (Andreas Karlsson, Computational Statistics, Vol. 22, 2007)
Table of Contents: PART I COMPUTATIONAL STATISTICS: What is Computational Statistics (James E. Gentle, Wolfgang Hardle, Yuichi Mori)/ PART II STATISTICAL COMPUTING: Basic Computational Algorithms (John Monaham); Random Number Generation (Pierre L'Ecuyer); MCMC Technology (Siddartha Chib); Numerical Linear Algebra (Lenka Cizkova); The EM Algorithm (Geoffrey McLachlan); Stochastic Optimization (James C. Spall); Transforms (Brani Vidakovic); Parallel Computing Techniques (Junji Nakano); Data Base Methodology (Oliver Gunther, Joachim Lenz); Statistical Languages (Tomoyuki Tarumi); High-Dimensional Visualization (Edward Wegman); Interactive Graphics (Jurgen Symanzik); The Grammar of Graphics (Leland Wilkinson); User Interfaces (Sigbert Klinke); Object Oriented Computing (Miroslav Virius)/PART III STATISTICAL METHODOLOGY: Cross Validation and Model Choice (Yuedong Wang); Bootstrap and Resampling (Enno Mammen); Simulation Techniques (Jack Kleijnen); Multivariate Density Estimation and Visualization (David Scott); Smoothing: Local Regression Techniques (Catherine Loader); Dimension Reduction Methods (Masahiro Mizuta); Generalized Linear Models (Marlene Muller); (Non) linear Regression Modelling (Pavel Cizek); Robustness Issues (P. Laurie Davies, Ursula Gather); Semiparametrics (Joel Horowitz); Computational Methods in Bayesian Analysis (Christian Robert); Data and Knowledge Mining (Adalbert X. Wilhelm); Tree Based Methods (Heping Zhang); Support Vector Machines (Klaus-Robert Muller); Statistical Learning Techniques (Peter Buhlmann); Computational Methods in Survival Analysis ( Toshinari Kamakura); PART IV SELECTED APPLICATIONS: Finance (Rafal Weron); Econometrics (Luc Bauwens); Bioinformatics (Iosif Vaisman); Functional MRI (William F. Eddy); Network Intrusion Detection (David Marchette)
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