MODEL HIDDEN MARKOV MULTISTATUS UNTUK MENGHITUNG PREMI ASURANSI KESEHATAN
Health conditions over time can be modeled using multistate Markov model. However, the information about health conditions are not always available, but there is another information related to these conditions. This study presents hidden Markov model to estimate transition intensities and observatio...
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| Main Authors: | , |
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| Format: | Theses and Dissertations NonPeerReviewed |
| Published: |
[Yogyakarta] : Universitas Gadjah Mada
2014
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| Subjects: | |
| Online Access: | https://repository.ugm.ac.id/128778/ http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=69145 |
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| Summary: | Health conditions over time can be modeled using multistate Markov model.
However, the information about health conditions are not always available, but there is
another information related to these conditions. This study presents hidden Markov
model to estimate transition intensities and observation probabilities for multistate
model where the true states are not observed. Maximum likelihood method is used to
estimate parameters in the model. Covariates will be fitted to transition intensities. The
estimation of transition intensities and transition probabilities, both with and without
covariates effect, will be used to calculate health insurance premium. By using this
method, it is expected to get premium value although the health condition data is not
available. This method will be applied to two datasets, data of patients visit in a clinic
in West Java and simulated data. Data analysis is done using R software. |
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