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Evolving Regression Models

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Evolutionary Statistical Procedures

Part of the book series: Statistics and Computing ((SCO))

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Abstract

Regression models are well established tools in statistical analysis which date back early to the eighteenth century. Nonetheless, problems involved in their implementation and application in a wide number of fields are still the object of active research. Preliminary to the regression model estimation there is an identification step which has to be performed for selecting the variables of interest, detecting the relationships of interest among them, distinguishing dependent and independent variables. On the other hand, generalized regression models often have nonlinear and non convex log-likelihood, therefore maximum likelihood estimation requires optimization of complicated functions. In this chapter evolutionary computation methods are presented that have been developed to either support or surrogate analytic tools if the problem size and complexity limit their efficiency.

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Authors and Affiliations

  1. Department of Communication and Social Research, Sapienza University of Rome, Via Salaria 113, 00198, Rome, Italy

    Prof. Roberto Baragona

  2. Department of Statistical Sciences, Sapienza University of Rome, Piazzale Aldo Moro 5, 00100, Roma, Italy

    Prof. Francesco Battaglia

  3. Department of Statistics, Ca’ Foscari University of Venice, Cannaregio 873, 30121, Venice, Italy

    Prof. Irene Poli

Authors
  1. Prof. Roberto Baragona
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  2. Prof. Francesco Battaglia
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  3. Prof. Irene Poli
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Corresponding author

Correspondence to Roberto Baragona .

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© 2011 Springer-Verlag Berlin Heidelberg

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Baragona, R., Battaglia, F., Poli, I. (2011). Evolving Regression Models. In: Evolutionary Statistical Procedures. Statistics and Computing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16218-3_3

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