A combination of variable selection and data mining techniques for high-dimensional statistical modelling

Christos Koukouvinos, Kalliopi Mylona and Christina Parpoula

International Journal of Information and Decision Sciences, 2013, vol. 5, issue 2, 154-168

Abstract: Variable selection is fundamental to statistical modelling in diverse fields of sciences. This paper deals with the problem of high-dimensional statistical modelling through the analysis of seismological data in Greece acquired during the years 1962-2003. The dataset consists of 10,333 observations and 11 factors, used to detect possible risk factors of large earthquakes. In our study, different statistical variable selection techniques are applied, while data mining techniques enable us to discover associations, meaningful patterns and rules. The statistical methods employed in this work were the non-concave penalised likelihood methods, SCAD, LASSO and Hard, the generalised linear logistic regression and the best subset variable selection. The applied data mining methods were three decision trees algorithms, the classification and regression tree (C%RT), the chi-square automatic interaction detection (CHAID) and the C5.0 algorithm. The way of identifying the significant variables in large datasets along with the performance of used techniques are also discussed.

Keywords: variable selection; non-concave penalised likelihood; data mining; decision making; high-dimensional statistical modelling; seismological data; Greece; risk assessment; large earthquakes; decision trees. (search for similar items in EconPapers)
Date: 2013
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