Abstract
Many systems involve progression through a series of distinct stages, such as disease or developmental stages. In ecological studies, often individuals such as small arthropods cannot be marked, so data are collected on cohort development. Multistage models for unmarked cohort data use a distribution for each stage duration and possibly stage-specific mortality rates. We generalize previous models and present computational methods for smoothed maximum likelihood estimation. The general model allows arbitrary distribution assumptions, stage-specific mortality, unobserved stages, and correlations between stage durations using Gaussian copulas. Monte Carlo integration of the stage distributions is used to approximate the probabilities needed for the likelihood. We establish a heuristic smoothing step for the simulated probabilities that yields a smooth approximate likelihood surface. For the case of classic grasshopper cohort data, we demonstrate AIC model selection to determine which among past arbitrary constraints are actually justified by the data. Finally, we demonstrate how estimates of stage distribution parameters depend on the unknown stage correlations.
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This work was partially supported by U.S. National Science Foundation Grant DEB-1021553.
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de Valpine, P., Knape, J. Estimation of General Multistage Models From Cohort Data. JABES 20, 140–155 (2015). https://doi.org/10.1007/s13253-014-0189-7
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DOI: https://doi.org/10.1007/s13253-014-0189-7