Evolutionary approaches for statistical modelling

In this paper, we describe some evolutionary approaches based on genetic algorithms to deal with the statistical model selection problem using completely data-driven algorithms. First, we propose an approach to select multivariate linear regression models as well as to build ARMA time-series models....

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Bibliographic Details
Main Authors: Minerva, T., Paterlini, S.
Format: Conference Proceeding
Language:English
Subjects:
Analytical models
Clustering algorithms
Genetic algorithms
Linear regression
Neural networks
Parameter estimation
Robustness
Statistical distributions
Stochastic processes
Time series analysis
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Summary:In this paper, we describe some evolutionary approaches based on genetic algorithms to deal with the statistical model selection problem using completely data-driven algorithms. First, we propose an approach to select multivariate linear regression models as well as to build ARMA time-series models. Then we introduce a methodology to tackle the clustering problem in a model-based framework. We report the results from several applications and from simulated data sets, and we compare the evolutionary approaches with some classical ones.
DOI:10.1109/CEC.2002.1004554