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In a second step, we adapt various bi-dimensional statistical models to these surfaces of data over age and time.
For each site we constructed marker concentration profiles and applied non-parametric (local quadratic) and parametric (two-compartmental, G2 → G1 → O) regression models to these profiles.
We fit Poisson N-mixture models to these data, quantified the bias associated with each combination, and evaluated if the parametric bootstrap goodness-of-fit (GOF) test can be used to indicate bias in parameter estimates.
The proposed model outperforms classical lifetime models to these data.
Ghitany et al. (2005) compared the fits of the ℳ O W and models to these data.
For a visual comparison, we provide PP plots of the fitted models to these data in Fig. 5.
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Edward Kaplan's research applies the tools of operations research and statistical modeling to these kinds of questions.
By fitting our mixture model to these data (Supplementary equations 9 11), we can re-cover the underlying mixture concentrations, despite the littoral end member consisting of terrestrial resources.
We fit the GBIII, BIII and other sub-models to these data by the method of maximum likelihood.
First, we fit the GEW model and some of its sub-models to these data by the method of maximum likelihood.
It is clear from Tables 5 and 6, and Figures 8 and 9 that the OGE-W, OGE-Fr and OGE-N models provide better fits than their sub-models to these two data sets.
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