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To test for changes in symptom ratings at 2 weeks and 4 weeks post-treatment, we used repeated measures linear regression models with measurement time as a categorical predictor and unstructured covariance between repeated measurements.
There exists a practical gap in connecting fungal growth models with measurement data.
In this paper, new observer design approaches are presented for Takagi-Sugeno (T-S) fuzzy models with measurement output noises.
This paper considers a class of estimators that can be used in dynamic models with measurement errors when external instruments may not be available or are weak.
For similar reasons, the total least squares (TLS) method has been generalized in the field of computational mathematics and engineering to maintain consistency of the parameter estimates in linear models with measurement errors of known distribution.
To assess the consistency of class structure over time, models with measurement thresholds constrained to be equal over time were compared with models allowing thresholds to vary, using the Satorra-Bentler Chi square difference test based on log-likelihood values and scaling correction factors obtained with the MLR estimator in Mplus [ 32]; see http://www.statmodel.com/chidiff.shtml.shtml
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One thing these temperatures can do is enable us to compare computer models with measurements.
The main study hypotheses will be explored using linear-mixed modeling with measurement occasions, patients, physiotherapists, and clinics as potential levels of the analysis.
A two-level random slopes (on age) model with measurements nested within pupils was used, with a continuous measure of TDS as the outcome.
This paper further validates this model with measurements from seven full-scale tests and four series of scaled model experiments.
The simulations described in this work were aimed to check the consistence of the model with measurements performed on a real sensor and evaluate the actual benefits of changing some geometrical parameters with respect to the original configuration.
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