Sentence examples for parametric variance from inspiring English sources

Polymerization kettle is the key controlled plant in ACR (Acrylate Copolymer Resin) production, which is a nonlinear time-delay system with parametric variance.

This model accounts for all the parametric variance in the data, including the minor individual differences among observers.

Significance of survival and diabetes incidence in NOD/Scid mice receiving various cell preparations was compared with that of control groups (diabetogenic T-cells alone) by the Kaplan-Meier test, and the significance of intra-group individual variations was determined by Kruskal-Wallis non parametric variance test.

Non parametric variance analyses between genetic polymorphisms were applied.

Non parametric variance analysis was done between genetic polymorphisms.

Figure 3: Nonparametric analyses are performed on these data, but the data are represented with parametric variance.

Thus the primary task of evaluating the parametric variance-based sensitivity is transformed to calculate the FPF at Gaussian nodes of the univariate functions, for which Monte Carlo (MC), Extended Monte Carlo (EMC) and Rejection Sampling (RS) are employed and compared here.

For estimating the effect of uncertain distribution parameter on the variance of failure probability function (FPF), the map from distribution parameters to FPF is built and the high efficient approximation form is extended to solve the parametric variance-based sensitivity index.

Then the parametric variance-based sensitivity index can be firstly expressed as the moments of the FPF, and the FPF is approximated by a product of the univariate functions of the distribution parameters, on which the moments of the FPF approximated by the univariate functions can be easily evaluated by the Gaussian integration using the values of the FPF at the Gaussian nodes.

To analyse differences between central tendencies, the Kruskal-Wallis-H-Test and Mann-Whitney-U-Test were used, which substituted parametric variance-analyses.

Thus, they provide better evaluation of the fitted data than other, more commonly used methods (e.g., error estimations from the parametric variance-covariance matrix).