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We consider two cases for the data generating probability measure: the model assumption and local deviations from the model assumption.
We consider two cases for the data generating probability measure: the model assumption and local contaminations or deviations from the model assumption.
Thirdly, the bias is due to the discrepancy between the model assumption and the actual reference ROI.
The predistorter is constructed using the indirect learning architecture, thereby eliminating the need for model assumption and parameter estimation of the PA [8, 9].
Firstly, the performance of the estimator strongly depends on the validity of the linearized model assumption and can become inaccurate and lead to filter instabilities if these assumptions are violated.
This means that such messages are not delivered during the first useful contact between MN1 and BS, in contrast with the model assumption and with the expected application behaviour.
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Additionally, the group heterogeneity in cluster and residual variances may require modelling to satisfy model assumptions and improve model fit.
The modelling steps include initial checking of model assumptions and the stability and parsimony of biomarker selection.
The main goal of our modeling approach was to address two violations of model assumptions and thereby improve model performance.
Details on the model assumptions and on the considered vector control strategies can be found in the Appendix S1.
Rigorous treatment of linear regression models, model assumptions, and various remedies for when these assumptions are violated.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com