A combination of variable selection and data mining techniques for high-dimensional statistical modelling
A combination of variable selection and data mining techniques for high-dimensional statistical modelling
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.
computing and mathematics, information systems and technology, management and business, operational management and marketing, policy and organisational management
154-168
Koukouvinos, Christos
9c88d32d-b519-4d78-a60d-66418cab1926
Mylona, Kalliopi
b44af287-2d9f-4df8-931c-32d8ab117864
Parpoula, Christina
6244ba93-0965-4177-8343-fedac13ac025
May 2013
Koukouvinos, Christos
9c88d32d-b519-4d78-a60d-66418cab1926
Mylona, Kalliopi
b44af287-2d9f-4df8-931c-32d8ab117864
Parpoula, Christina
6244ba93-0965-4177-8343-fedac13ac025
Koukouvinos, Christos, Mylona, Kalliopi and Parpoula, Christina
(2013)
A combination of variable selection and data mining techniques for high-dimensional statistical modelling.
International Journal of Information and Decision Sciences, 5 (2), .
(doi:10.1504/IJIDS.2013.053799).
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.
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Published date: May 2013
Keywords:
computing and mathematics, information systems and technology, management and business, operational management and marketing, policy and organisational management
Organisations:
Statistics, Statistical Sciences Research Institute
Identifiers
Local EPrints ID: 354149
URI: http://eprints.soton.ac.uk/id/eprint/354149
ISSN: 1756-7017
PURE UUID: b3168248-d3b0-48e2-9f95-2df01aa30e99
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Date deposited: 03 Jul 2013 11:16
Last modified: 14 Mar 2024 14:14
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Contributors
Author:
Christos Koukouvinos
Author:
Kalliopi Mylona
Author:
Christina Parpoula
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