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A cluster tree based model selection approach for logistic regression classifier
Date
2018-01-01
Author
Tanju, Ozge
Kalaylıoğlu Akyıldız, Zeynep Işıl
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Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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Model selection methods are important to identify the best approximating model. To identify the best meaningful model, purpose of the model should be clearly pre-stated. The focus of this paper is model selection when the modelling purpose is classification. We propose a new model selection approach designed for logistic regression model selection where main modelling purpose is classification. The method is based on the distance between the two clustering trees. We also question and evaluate the performances of conventional model selection methods based on information theory concepts in determining best logistic regression classifier. An extensive simulation study is used to assess the finite sample performances of the cluster tree based and the information theoretic model selection methods. Simulations are adjusted for whether the true model is in the candidate set or not. Results show that the new approach is highly promising. Finally, they are applied to a real data set to select a binary model as a means of classifying the subjects with respect to their risk of breast cancer.
Subject Keywords
Statistics, Probability and Uncertainty
,
Modelling and Simulation
,
Statistics and Probability
,
Applied Mathematics
URI
https://hdl.handle.net/11511/36456
Journal
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
DOI
https://doi.org/10.1080/00949655.2018.1437442
Collections
Department of Statistics, Article
Citation Formats
IEEE
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BibTeX
O. Tanju and Z. I. Kalaylıoğlu Akyıldız, “A cluster tree based model selection approach for logistic regression classifier,”
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
, vol. 88, no. 7, pp. 1394–1414, 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/36456.