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The smoothing parameter can either be selected using generalized cross-validation (GCV) or (restricted) maximum likelihood [ RE ML] (Wood, 2006).
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Degrees of freedom for the thin-plate spline were selected using generalized cross-validation.
Cell clones were selected using cloning cylinders.
The students were selected using convenience sampling.
Factors were selected and evaluated using generalized linear mixed models (identity link for the number of meals per day, the quantity of milk consumed per day, the logarithm of the milk quantity consumed per meal, and the logarithm of the time spent feeding per meal and per day; log link for the number of feeding events per meal).
We calculated a propensity score for preterm birth conditional on a GAM spline of conception date with an unmatched logistic regression model using the reference population, where the smoothing parameters were selected by generalized cross-validation.
The differences among normalized data can then be evaluated using generalized linear models [1], [8], [9].
Data will be analyzed using generalized estimating equation, a special generalized linear models technique.
Basis functions and knot points are also determined for each selected descriptor using generalized cross validation after frequency analysis.
Analyses will be carried out using generalized linear models.
Data were analyzed using generalized linear mixed models.
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