By Peter Hall (auth.), Paula Brito (eds.)
This quantity offers fresh methodological advancements in Applied/Computational records. a variety of themes is roofed together with Advances on Statistical Computing Environments, equipment for type and Clustering, Computation for Graphical types and Bayes Nets, Computational Econometrics, Computational facts and knowledge Mining, facts for Finance and coverage, info Retrieval for textual content and photographs, wisdom Extraction by way of types, version choice Algorithms, types for Latent category Detection, a number of checking out approaches, Random seek Algorithms, strong statistics and sign Extraction and Filtering. in addition to structural and theoretical effects, the e-book offers a large choice of purposes in fields equivalent to time-series research, econometrics, finance and coverage, biology, micro-array research, astronomy, textual content research and alcohol reviews. The CD-ROM accompanying additionally it is educational texts on Computational Finance and Writing R applications. Combining new methodological advances with a large choice of genuine purposes, this quantity is specifically worthwhile for researchers and practitioners, supplying new analytical instruments valuable in theoretical study and day-by-day perform in computational information and utilized records usually.
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Additional resources for COMPSTAT 2008: Proceedings in Computational Statistics
Soc. Ser. B 65, 869-886. J. (1973): The estimation of frequency. J. Appl. Prob. 10, 510-519. J. (1974): Time-series analysis. System identiﬁcation and time-series analysis. IEEE Trans. Automatic Control AC-19, 706-715. , KOEN, C. and LOMBARD, F. (2004): An analysis of pulsation periods of long-period variable stars. J. Roy. Statist. Soc. Ser. C 56, 587-606. , RICHTER, G. and WENZEL, W. (1985): Variable Stars. Springer, Berlin. , TITZ, R. and WIESE, T. (2007): Characterization of COROT Target Fields with BEST: Identiﬁcation of Periodic Variable Stars in the IR01 Field.
Part of this is due to the success of the R system for statistical computing, which provides many features which provide adequate support for practical statistical applications and research. An evolutionary process has resulted in selective pressure for R and eliminated a number of interesting alternatives. This is not to say that the ﬁeld is dead. There are some very interesting and more subtle improvements and experiments found in some of the R packages. These user interfaces provide interesting and more direct experiments focused on enhancing particular practices.
This means that users can introduce new “core” data types within their code and they can be used in the same manner as the core data types provided by our “new” environment. This extensibility allows others outside of the language developers to perform new experiments on the system itself and to disseminate them to others without needing to alter the system. This gives us a great deal of ﬂexibility to handle new tasks and explore alternative approaches to computing. This is also important if we are to foster research on the topic of statistical computing environments themselves, which is necessary if statistical computing is to continue to evolve.
COMPSTAT 2008: Proceedings in Computational Statistics by Peter Hall (auth.), Paula Brito (eds.)