Hierarchical modelling of multivariate survival data

Hierarchical models based on conditional independence are investigated as a means of modelling multivariate survival times. The model structure follows Clayton (1978), Hougaard (1986b), and Oakes (1986, 1989). Both approximate Bayesian and maximum likelihood estimation in these models is investigated via simulation. Predicting a component of a response vector on the basis of other components of the response and other vectors is also studied, using Bayes and empirical Bayes methods. Application to real data is detailed, using the techniques of estimation and prediction discussed.

Name thesis:degree_name

Master of Science - MSc

Level thesis:degree_level

master's

Discipline thesis:degree_discipline

Statistics

Grantor dc:publisher

University of British Columbia

Year dc:date

1991

Author dc:creator

• Gustafson, Paul Antony

Statement dc:rights

• For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.

Language dc:language

eng

Handle dc:identifier

http://hdl.handle.net/2429/1649

OAI identifier oai:identifier

oai:circle.library.ubc.ca:2429/1649