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One model within the class will possibly describe the data more adequately than the others which will be the "true model" but we do not know its identification.
In order to evaluate the performance of the different methods, we now run a simulation where parameters obtained from the Iris dataset are assumed to be the "true model".
Although we have focused on the exponential distribution in this study, we noted that this is only theoretical and may not exactly be the true model in real scenarios (please see the discussion section for further details).
This is a method of accepting the models which are most likely to be the true model and not accepting any unnecessarily complicated model.
On the other hand, the one-QTL model (single marker analysis or the interval mapping method) is often used in initial scan even though it may not be the true model.
If the method is accurately estimating the probability that the one-divergence model is correct given the data, then the one-divergence model should be the true model in approximately 900 of the 1000 replicates.
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given that the Rasch Model is the true model and the subsample sizes n (j)→∞ (ibid., p. 128).
In the pioneering work of Atkinson and Fedorov (1975) [4,3], the discrimination criterion T optimality was introduced for pairwise discrimination between models with the assumption that one of the models being the true model.
Additionally we provided the Akaike weights, which estimate the likelihood that a specific model is the true model, assuming that the true model is in the set of examined models [14].
Therefore, nnSEM is the true model.
Therefore, both nnSEM and SEM are the true model.
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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