Evolutionary approaches for statistical modelling

Author

Minerva, Tommaso ; Paterlini, Sandra

Author_Institution

Dipt. di Sci. Sociali, Univ. of Modena & Reggio Emilia, Italy

Volume

2

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

2023

Lastpage

2028

Abstract

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

Keywords

autoregressive moving average processes; genetic algorithms; modelling; pattern clustering; statistical analysis; time series; ARMA time-series models; autoregressive moving average; data-driven algorithms; evolutionary statistical modelling; genetic algorithms; model-based clustering; multivariate linear regression models; statistical model selection problem; Analytical models; Clustering algorithms; Genetic algorithms; Linear regression; Neural networks; Parameter estimation; Robustness; Statistical distributions; Stochastic processes; Time series analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2002. CEC '02. Proceedings of the 2002 Congress on

Conference_Location

Honolulu, HI

Print_ISBN

0-7803-7282-4

Type

conf

DOI

10.1109/CEC.2002.1004554

Filename

1004554