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Evolutionary Computing in Statistical Data Analysis

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Foundations of Computational Intelligence Volume 3

Part of the book series: Studies in Computational Intelligence ((SCI,volume 203))

Abstract

Evolutionary computing methods are being used in a wide field domain with increasing confidence and encouraging outcomes. We want to illustrate how these new techniques have influenced the statistical theory and practice concerned with multivariate data analysis, time series model building and optimization methods for statistical estimates computation and inference in complex systems. The distinctive features all these subject topics have in common are the large number of alternatives for model choice, parametrization over high dimensional discrete spaces and lack of convenient properties that may be assumed to hold at least approximately about the data generating process. Evolutionary computing proved to be able to offer a valuable framework to deal with complicated problems in statistical data analysis and time series analysis and we shall draw a wide though by no means exhaustive list of topics of interest in statistics that have been successfully handled by evolutionary computing procedures. Specific issues will be concerned with variable selection in linear regression models, non linear regression, time series model identification and estimation, detection of outlying observations in time series as regards both location and type identification, cluster analysis and grouping problems, including clusters of directional data and clusters of time series. Simulated examples and applications to real data will be used for illustration purpose through the chapter.

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Authors and Affiliations

  1. Department of Sociology and Communication, Sapienza University of Rome, Via Salaria 113, Rome, Italy

    Roberto Baragona

  2. Department of Statistics Probability and Applied Statistics, Sapienza University of Rome, Piazzale Aldo Moro 5, 00100, Rome, Italy

    Francesco Battaglia

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  1. Roberto Baragona
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Editors and Affiliations

  1. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, P.O. Box 2259, 98071-2259, Auburn, Washington, USA

    Ajith Abraham

  2. College of Business Administration Quantitative and Information System Department, Kuwait University, P.O. Box 5486, 13055, Safat, Kuwait

    Aboul-Ella Hassanien

  3. Université Paris XII Fac. Sciences, LERISS, 61 avenue du Général de Gaulle Building P2 - Room 350, 94010, Créteil, France

    Patrick Siarry

  4. Department of Computer Science, University of Pretoria, 0002, Pretoria, South Africa

    Andries Engelbrecht

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Baragona, R., Battaglia, F. (2009). Evolutionary Computing in Statistical Data Analysis. In: Abraham, A., Hassanien, AE., Siarry, P., Engelbrecht, A. (eds) Foundations of Computational Intelligence Volume 3. Studies in Computational Intelligence, vol 203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01085-9_12

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