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), 195-209.
Full text not available from this repository.
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.
|Keywords:||variable selection, generalized linear model, nonconcave penalized likelihood, high-dimensional dataset, trauma|
|Subjects:||H Social Sciences > HA Statistics
Q Science > QA Mathematics
R Medicine > RA Public aspects of medicine
|Divisions:||Faculty of Social and Human Sciences > Mathematical Sciences > Statistics
|Date Deposited:||04 Apr 2012 14:11|
|Last Modified:||27 Mar 2014 20:20|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
Actions (login required)