<p> We propose a data-driven estimation algorithm in survival mixture model. The objective of
this study is to provide an alternative fitting procedure to the conventional EM algorithm.
The EM algorithm is the classical ML fitting of the parametric mixture model. If the initial
values for the EM algorithm are not properly chosen, the maximizers might be local or
divergent. Traditionally, initial values are given manually according to experience or a gridpoint
search. This is a heavy burden for a high-dimensional data sets. Also, specifying the
ranges of parameters for a grid-point search is difficult. To avoid the specification of initial
values, we employ the random partition. Then, improvement of fitting is adjusted according
to model specification. This process is repeated a large number of times, so it is computer intensive.
The large repetitions makes the solution more likely to be the global maximizer,
and it is driven purely by the data. We conduct a simulation study for three cases of
two-component Log-Normal, two-component Weibull, and two-component Log-Normal and
Wei bull, in order to illustrate the effectiveness of the proposed algorithm. Finally, we apply
our algorithm to a breast cancer study data which follows a cure model. The program is
written in R. It calls existing R functions, so it is flexible to use in regression situations where
model formula must be specified. </p> / Thesis / Master of Science (MSc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/21298 |
| Date | 12 1900 |
| Creators | Zhang, Jin |
| Contributors | Zhu, Rong, Statistics |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
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