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Use of parametric models (linear regression) and non-parametric models (Mann Kendall) enabled us to establish temporal trends in soil water availability and changes in productivity.
Data requirements (number of samples, sampling frequency) for the use of parametric models are discussed.
One way to control the complexity of such models is through the use of parametric models.
To enable the use of parametric models, we computed the change in seizure frequency as the primary outcome variable, rather than an absolute seizure number, since this was more likely to be normally distributed.
When a patient belongs to a larger population in which the parameter distributions are not Gaussian, as found here, the use of parametric models based on only single-point parameter estimates has no way to evaluate and optimize the expected precision with which a dosage regimen will hit a target.
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Thus, use of parametric modeling techniques, such as analysis of variance, to characterize intra- and interlaboratory variability was precluded.
The model-based design for this auto-tuning makes uses of parametric models of FOPDT (i.e. first-order-plus-dead-time) and of SOPDT (i.e. second-order-plus-dead-time) dynamics.
The paper explores the potential uses of parametric modeling to embed construction and structural design knowledge in the form of generative rules and feedback rule-checking functions.
It presents a classification of parametric models currently used to describe marine vessel dynamics.
The AIC and BIC can be used for model comparison among a class of parametric models having the same dependent variable, but with different numbers of independent parameters.
Use of flexible parametric models to more easily capture the shape of the underlying hazards function and extrapolate future survival estimates.
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