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Mixed-effects population models [ 8] were fitted to the Org25435 plasma concentration versus time data using the first order estimation method of the program NONMEM [ 9].
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All those existing order estimation methods were not proposed for speech signals; so they do not consider or take advantage of the special characteristics of speech.
Group contribution first-order estimation methods are coupled with property clustering methods to define the targets of the property search space and to predict the properties of the synthesized formulations.
NONMEM's first order conditional estimation method was used.
The method selected for minimization was First Order Conditional Estimation method with Interaction (FOCEI) [29].
NONMEM's first order conditional estimation method with interaction was used for model development.
Modeling was performed in NONMEM® v. 7.2 [ 23] using the first order conditional estimation method with interaction.
The first order conditional estimation method (FOCE) with INTERACTION and a user-defined model (ADVAN6 nonmem Subroutine) were used for the analysis.
All modelling was performed in nonmem, version 7 (ICON, ICON Development Solutions, Ellicott City, MD, USA), using the first order conditional estimation method with interaction [ 23].
Data analysis was performed using the software NONMEM, version 7.2 (ICON Development Solutions, Ellicott City, USA) [ 14], using the first order conditional estimation method with interaction (FOCE INTER).
The model building was conducted using the first order conditional estimation method with interaction and the Laplacian estimation method for the M3 method to avoid bias from the large fraction of data below the limit of quantification [ 22].
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