Date of Award:
5-1998
Document Type:
Thesis
Degree Name:
Master of Science (MS)
Department:
Mathematics and Statistics
Committee Chair(s)
Olcay Akman
Committee
Olcay Akman
Committee
Richard Cutler
Committee
Joe Koebbe
Abstract
When analyzing data in a survival setting, whether of people or objects, one of the assumptions made is that the population is homogeneous. This is not true in reality and certain adjustments can be made in the model to account for heterogeneity. Frailty is one method of dealing with some of this heterogeneity. It is not possible to measure frailty directly and hence it can be very difficult to determine which frailty model is appropriate for the data in interest. This thesis investigates three model selection methods in their effectiveness at determining which frailty distribution best describes a given set of data. The model selection methods used are the Bayes factor, neural networks, and classification trees. Results favored classification trees. Very poor results were observed with neural networks.
Checksum
e9ad30086b85383d63afb6a4f3ad97f1
Recommended Citation
Lundell, Jill F., "On the Model Selection in a Frailty Setting" (1998). All Graduate Theses and Dissertations, Spring 1920 to Summer 2023. 7110.
https://digitalcommons.usu.edu/etd/7110
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