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We used both estimators to estimate the LOD curve for several simulated pedigrees, for 5 different runs of our Gibbs sampler.
A large difference in the point estimates between both estimators would highlight that time-invariant unobservables such as motivation, ambition, and ability play important roles in the training decisions.
Simulation results showed that both estimators could precisely estimate θ m when θ m is small.
They found (their Tables 2 and 3) that both estimators tended to under-estimate the true mean of their population by 2 4%, the bias tending to decline with increasing sample size.
For both estimators, we first compute local estimates at the different frequency components, and then we combine them in order to find the global estimates.
For both estimators, the 95% CIs associated with population size estimates were calculated with the log-transformation suggested by Burnham and used by Chao (15, 16 ).
In all models, individuals with partially missing item level data were included, since estimation of missing data patterns is possible under both estimators (traditional ML and WLSMV).
Indeed, whether the SNR is assumed known or with a Gaussian estimation error with variance σ ω 2 = 1, both estimators show the same results.
Both estimators were found to be robust.
Both estimators use a one-dimensional population balance model.
Both estimators were applied using N=1 (i.e. single snapshot).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com