| Summary: | 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.
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