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Variable selection via nonconcave penalized likelihood in high dimensional medical problems

Variable selection via nonconcave penalized likelihood in high dimensional medical problems
Variable selection via nonconcave penalized likelihood in high dimensional medical problems
Variable selection is fundamental to high-dimensional statistical modelling in diverse fields of sciences. Specially in health studies, many potential factors are 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 dataset consists of 6334 observations and at most 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. The performed analysis reveals several distinct advantages of the nonconcave penalized likelihood methods over the traditional model selection techniques.
0973-7545
1-11
Mylona, K.
b44af287-2d9f-4df8-931c-32d8ab117864
Koukouvinos, C.
3c626a53-575f-4c62-9b9a-a949f717764b
Theodoraki, E.-M.
cfd49481-b24a-447a-9b17-9aefd5ee3362
Katsaragakis, S.
0e41c037-bf34-4944-a763-ebd6508da3c4
Mylona, K.
b44af287-2d9f-4df8-931c-32d8ab117864
Koukouvinos, C.
3c626a53-575f-4c62-9b9a-a949f717764b
Theodoraki, E.-M.
cfd49481-b24a-447a-9b17-9aefd5ee3362
Katsaragakis, S.
0e41c037-bf34-4944-a763-ebd6508da3c4

Mylona, K., Koukouvinos, C., Theodoraki, E.-M. and Katsaragakis, S. (2009) Variable selection via nonconcave penalized likelihood in high dimensional medical problems. International Journal of Applied Mathematics and Statistics, 14 (J09), 1-11.

Record type: Article

Abstract

Variable selection is fundamental to high-dimensional statistical modelling in diverse fields of sciences. Specially in health studies, many potential factors are 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 dataset consists of 6334 observations and at most 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. The performed analysis reveals several distinct advantages of the nonconcave penalized likelihood methods over the traditional model selection techniques.

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Published date: June 2009
Organisations: Statistics

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Local EPrints ID: 336752
URI: http://eprints.soton.ac.uk/id/eprint/336752
ISSN: 0973-7545
PURE UUID: a19e0a7a-f971-4e6c-817d-a54376e708f5

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Date deposited: 04 Apr 2012 14:24
Last modified: 08 Jan 2022 05:52

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Contributors

Author: K. Mylona
Author: C. Koukouvinos
Author: E.-M. Theodoraki
Author: S. Katsaragakis

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