dc.contributor.advisor |
Lee, Alan |
en |
dc.contributor.author |
Hirose, Yuichi |
en |
dc.date.accessioned |
2007-06-13T04:13:10Z |
en |
dc.date.available |
2007-06-13T04:13:10Z |
en |
dc.date.issued |
2005 |
en |
dc.identifier |
THESIS 05-241 |
en |
dc.identifier.citation |
Thesis (PhD--Statistics)--University of Auckland, 2005 |
en |
dc.identifier.uri |
http://hdl.handle.net/2292/499 |
en |
dc.description |
Full text is available to authenticated members of The University of Auckland only. |
en |
dc.description.abstract |
In this thesis, we investigate the efficiency of the semi-parametric maximum likelihood estimator in the context of generalized case-control studies. We introduce the idea of a multi-sample model and show that it enables us to treat a number of variations of the basic case-control study under the same framework in a natural way. For example, data from a case-control study are a multi-sample, since they consist of two independent samples, one from the case population and one from the control population. Some missing data can also be treated as a multi-sample. We select full data from the full data population and partial data from the other population. Moreover, data gathered using an outcome-dependent two-phase sampling design
can also be regarded as a multi-sample. We show that the theory of M-estimation for an i.i.d. model can be extended naturally to multi-sample models, and treat maximum likelihood in these models as a special case of M-estimation. The efficiency of the maximum likelihood estimator can then be studied using the theory of M-estimators in a multi-sample model. Scott & Wild (1997, 2001) use a profile likelihood approach to calculate the semiparametric maximum likelihood estimator in generalized case-control studies. The resulting estimating equations cannot be treated using standard M-estimator theory, since the estimating functions depend on the sample size. We extend the
standard treatment of estimating functions to include the derivative of the profile log-likelihood so that the maximum likelihood estimator, the solution to its corresponding estimating equation, is a special case of an M-estimator. We then demonstrate that the semi-parametric MLE is the most efficient among the class of extended M-estimators. |
en |
dc.language.iso |
en |
en |
dc.publisher |
ResearchSpace@Auckland |
en |
dc.relation.ispartof |
PhD Thesis - University of Auckland |
en |
dc.relation.isreferencedby |
UoA99149156814002091 |
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dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. |
en |
dc.rights |
Restricted Item. Available to authenticated members of The University of Auckland. |
en |
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
en |
dc.title |
Efficiency of the semi-parametric maximum likelihood estimator in generalized case-control studies |
en |
dc.type |
Thesis |
en |
thesis.degree.discipline |
Statistics |
en |
thesis.degree.discipline |
Statistics |
en |
thesis.degree.grantor |
The University of Auckland |
en |
thesis.degree.level |
Doctoral |
en |
thesis.degree.name |
PhD |
en |
dc.rights.holder |
Copyright: The author |
en |
dc.identifier.wikidata |
Q111963790 |
|