Title :
Evolutionary approaches for statistical modelling
Author :
Minerva, Tommaso ; Paterlini, Sandra
Author_Institution :
Dipt. di Sci. Sociali, Univ. of Modena & Reggio Emilia, Italy
fDate :
6/24/1905 12:00:00 AM
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;
Conference_Titel :
Evolutionary Computation, 2002. CEC '02. Proceedings of the 2002 Congress on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7282-4
DOI :
10.1109/CEC.2002.1004554
