Abstract
The problem space in epidemiological research is characterized by large datasets with many variables as candidates for logistic regression model building. Out of these variables the variable combinations which form a sufficient logistic regression model have to be selected. Usually methods like stepwise logistic regres‘sion apply.
These methods deliver suboptimal results in most cases, because they cannot screen the entire problem space which is formed by different variable combinations with their resulting case set. Screening the entire problem space causes an enormous effort in computing power. Furthermore the resulting models have to be judged. This paper describes an approach for calculating the complete problem space using a computer grid as well as quality indicators for judgement of every particular model in order to find the best fitting models.
We are using this system for epidemiological studies addressing specific problems in human epidemiology.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Dubitzky, W., Mccourt, D., Galushka, M., Romberg, M., Schuller, B.: Grid-Enabled Data Warehousing For Molecular Engineering. Parallel Computing 30, 1019–1035 (2004)
Eu Data Grid (05-22-2007), http://eu-datagrid.web.cern.ch/eu-datagrid/
Foster, I.: What Is The Grid? A Three Point Checklist Global Grid Forum (05-15-2007), http://www-fp.mcs.anl.gov/~foster/articles/whatisthegrid.pdf
Grid Physics Network (05-22-2007), http://www.griphyn.org/
Harrell, F.E.: Regression Modeling Strategies. Springer, New York (2001)
Hosmer, D.W., And Lemeshow, S.: Applied Logistic Regression, 2nd edn. Wiley, New York (2000)
Myers, D.S., And Cummings, M.P.: Necessity Is The Mother Of Invention: A Simple Grid Computing System Using Commodity Tools. J. Parallel Distrib. Comput. 63, 578–589 (2003)
The Globus Toolkit (05-25-2007), http://www.globus.org
The Unicore Project (05-25-2007), http://unicore.sourceforge.net
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Roeder, S.W., Richter, M., Herbarth, O. (2008). Model Screening: How to Choose the Best Fitting Regression Model?. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4985. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69162-4_91
Download citation
DOI: https://doi.org/10.1007/978-3-540-69162-4_91
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69159-4
Online ISBN: 978-3-540-69162-4
eBook Packages: Computer ScienceComputer Science (R0)