A Bayesian approach for parameter estimation in multi-stage models
Multi-stage time evolving models are common statistical models for biological systems, especially insect populations. In stage-duration distribution models, parameter estimation for the models use the Laplace transform method. This method involves assumptions such as known constant shapes, known...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
TAYLOR & FRANCIS INC
2019
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/79606 |
| _version_ | 1848764080222896128 |
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| author | Pham, Hoa Nur, Darfiana Pham, Huong TT Branford, Alan |
| author_facet | Pham, Hoa Nur, Darfiana Pham, Huong TT Branford, Alan |
| author_sort | Pham, Hoa |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Multi-stage time evolving models are common statistical models for
biological systems, especially insect populations. In stage-duration distribution models, parameter estimation for the models use the Laplace
transform method. This method involves assumptions such as known
constant shapes, known constant rates or the same overall hazard rate
for all stages. These assumptions are strong and restrictive. The main
aim of this paper is to weaken these assumptions by using a Bayesian
approach. In particular, a Metropolis-Hastings algorithm based on deterministic transformations is used to estimate parameters. We will use
two models, one which has no hazard rates, and the other has stagewise constant hazard rates. These methods are validated in simulation
studies followed by a case study of cattle parasites. The results show that
the proposed methods are able to estimate the parameters comparably
well, as opposed to using the Laplace transform methods. |
| first_indexed | 2025-11-14T11:13:40Z |
| format | Journal Article |
| id | curtin-20.500.11937-79606 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:13:40Z |
| publishDate | 2019 |
| publisher | TAYLOR & FRANCIS INC |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-796062020-06-15T00:23:51Z A Bayesian approach for parameter estimation in multi-stage models Pham, Hoa Nur, Darfiana Pham, Huong TT Branford, Alan Science & Technology Physical Sciences Statistics & Probability Mathematics Bayesian analysis destructive samples multi-stage models stage duration stage frequency data TRANSFORM ESTIMATION STAGE TIMES Multi-stage time evolving models are common statistical models for biological systems, especially insect populations. In stage-duration distribution models, parameter estimation for the models use the Laplace transform method. This method involves assumptions such as known constant shapes, known constant rates or the same overall hazard rate for all stages. These assumptions are strong and restrictive. The main aim of this paper is to weaken these assumptions by using a Bayesian approach. In particular, a Metropolis-Hastings algorithm based on deterministic transformations is used to estimate parameters. We will use two models, one which has no hazard rates, and the other has stagewise constant hazard rates. These methods are validated in simulation studies followed by a case study of cattle parasites. The results show that the proposed methods are able to estimate the parameters comparably well, as opposed to using the Laplace transform methods. 2019 Journal Article http://hdl.handle.net/20.500.11937/79606 10.1080/03610926.2018.1465090 English TAYLOR & FRANCIS INC restricted |
| spellingShingle | Science & Technology Physical Sciences Statistics & Probability Mathematics Bayesian analysis destructive samples multi-stage models stage duration stage frequency data TRANSFORM ESTIMATION STAGE TIMES Pham, Hoa Nur, Darfiana Pham, Huong TT Branford, Alan A Bayesian approach for parameter estimation in multi-stage models |
| title | A Bayesian approach for parameter estimation in multi-stage models |
| title_full | A Bayesian approach for parameter estimation in multi-stage models |
| title_fullStr | A Bayesian approach for parameter estimation in multi-stage models |
| title_full_unstemmed | A Bayesian approach for parameter estimation in multi-stage models |
| title_short | A Bayesian approach for parameter estimation in multi-stage models |
| title_sort | bayesian approach for parameter estimation in multi-stage models |
| topic | Science & Technology Physical Sciences Statistics & Probability Mathematics Bayesian analysis destructive samples multi-stage models stage duration stage frequency data TRANSFORM ESTIMATION STAGE TIMES |
| url | http://hdl.handle.net/20.500.11937/79606 |