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A comparative study of variable selection procedures applied in high dimensional medical problems

Koukouvinos, C., Mylona, K. and Vonta, F. (2008) A comparative study of variable selection procedures applied in high dimensional medical problems Journal of Applied Probability & Statistics, 3, (2), pp. 195-209.

Record type: Article


In health studies, many potential factors are usually introduced to determine an outcome variable. In our study, different statistical methods are applied to analyze trauma annual data, collected by 30 General Hospitals in Greece. The first dataset consists of 1681 observations and 76 factors and the second of 6334 observations and 131 factors, that include demographic, transport and intrahospital data. The statistical methods employed in this work were the nonconcave penalized likelihood methods, SCAD, LASSO, and Hard, the generalized linear logistic regression, and the best subset variable selection, used to detect possible risk factors of death. A variety of different statistical models are considered, with respect to the combinations of factors and the number of observations. A comparative survey reveals differences between results and execution times of each method, and the analysis produces models that identify the significant prognostic factors affecting death from trauma.

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Published date: 2008
Keywords: variable selection, generalized linear model, nonconcave penalized likelihood, high-dimensional dataset, trauma
Organisations: Statistics


Local EPrints ID: 336713
ISSN: 1930-6792
PURE UUID: 50afe121-f877-4bee-b64e-6067051fc2db

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Date deposited: 04 Apr 2012 14:11
Last modified: 18 Jul 2017 06:06

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Author: C. Koukouvinos
Author: K. Mylona
Author: F. Vonta

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