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Handbook of Big Data Analytics - Google книги
It ends with an interesting section on privacy and security. Chapters 10 and 11 are about graphics: one is on interactive and dynamic graphics exploring visual representations of datasets, the other is on a grammar of graphs consisting of seven components, from variables to aesthetics specifying subtasks required to produce graphics from data.
Part II concludes with a chapter on statistical user interface, perhaps the weakest in the volume, which compares incompletely seven software packages and discusses rules for good user interface design, and another, very formal, chapter on object oriented computing, describing the unified modelling language. It is followed by a very thorough survey on bootstrap and resampling methods, including a discussion on bootstrapping for dependent data. Chapter 3 deals with design and analysis of Monte Carlo experiments, including a section on applications of Kriging interpolation.
The next two chapters are on multivariate density estimation and visualization including trivariate functions , and on smoothing and local regression techniques including likelihood smoothing and are sound reviews of these areas. Chapter 6 is on dimension reduction methods and covers methods which do not distinguish between response and explanatory variables e. The next four chapters are on modelling: generalized linear models, linear and non-linear regression modelling, robust statistics, and semiparametric models.
Some overlapping between these four chapters and the first one in Part III is inevitable, and perhaps the five of them could have been combined into two bigger chapters. Chapter 11 is a survey of Bayesian computational methods covering point estimation, tests of hypotheses, model choice, and a comprehensive review of Monte Carlo methods and their applications illustrating them with several interesting examples; it is followed by a brief chapter on computational methods in survival analysis, which does not cover interval censored problems nor frailty.
Part IV examines applications of computational statistics to particular fields. It starts with two chapters from the financial area: one on computational intensive modelling of financial data using heavy-tailed distributions, and one on Econometrics, covering a wide range of models, from extensions of multivariate probit and logit models to finite mixture models.
Chapter 3 is an analysis of the statistical and geometrical properties of the structure of protein molecules, followed by a review of methods for analysing functional magnetic resonance imaging.
ISBN 13: 9783642215506
The last chapter examines statistical methods for computer network intrusion detection—an area of application of data mining techniques, and a clear example of the type of datasets and problems shaping the future of computational statistics. Inevitably for a handbook like this, there is some variation in quality among the chapters, though the vast majority are very well written and convey the main points of their subjects clearly and efficiently.
There are very few typographic and spelling mistakes, and the indexes are well prepared. There are also omissions that would have been of interest to some readers, for instance symbolic computation, multilevel models, spatial methods and geographic information systems, and data disclosure problems; however, the editors mention that handbooks on both theoretical aspects and applications of computational statistics may appear in future. This is an impressive and useful reference volume which I would recommend to anyone interested in computational statistics.
The editors should be congratulated for bringing together a very good snapshot of the current state of a rapidly evolving subject. Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide. Sign In or Create an Account. Sign In. Advanced Search.
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Volume Article Contents. Handbook of Computational Statistics—Concepts and Methods. Mario Cortina Borja. Oxford Academic.
Google Scholar. Cite Citation. Permissions Icon Permissions. References 1. New York: Springer-Verlag, Statistical Computing. New York: Marcel Dekker Inc, Rao CR ed.