Publication Details

Category Text Publication
Reference Category Book chapters
DOI 10.1007/978-3-540-69162-4_91
Title (Primary) Model screening: how to choose the best fitting regression model?
Title (Secondary) Neural Information Processing, ICONIP 2007, Part II
Author Röder, S. ORCID logo ; Richter, M.; Herbarth, O.
Publisher Ishikawa, M.; Doya, K.; Miyamoto, H.; Yamakawa, T.
Source Titel Lecture Notes in Computer Science
Year 2008
Department EXPOEPID
Volume 4985
Page From 876
Page To 883
Language englisch
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.

Persistent UFZ Identifier https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=1379
Röder, S., 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, Part II
Lect. Notes Comput. Sci. 4985
Springer, Berlin, Heidelberg, New York, p. 876 - 883 10.1007/978-3-540-69162-4_91