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Unlike Hida's approach, the generalized Brownian functionals are defined and studied through the "generalized function-test function" pairing instead of employing the U-functionals.
Buchanan and her colleagues recently adopted a different approach, the generalized Pareto distribution, to incorporate all hourly water-level observations above the 99th percentile.
In yet another approach, the generalized- model is shown to provide excellent fits to different types of atmospheric noise [23].
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A theorem in EVT shows that F (x) asymptotically approaches the generalized Pareto distribution (GPD), {F}^{left[mu right]}(x approx {W}_{mu; sigma, gamma }(x)=1-{left(1+gamma frac{x-mu }{sigma}right)}^{-1/gamma }, (2 where μ, γ, and σ represent location, shape, and scale parameters, respectively.
A numerical study using a hemisphere test reveals that the Voigt-Kelvin approach and the generalized Maxwell approach lead to similar results for the prediction of wrinkling behavior.
A support-space/stochastic Galerkin approach and the generalized polynomial chaos expansion (GPCE) approach are considered for input output uncertainty representation.
We recently developed a general combinatorial approach, namely the generalized multifactor dimensionality reduction (GMDR) method, which can entertain both qualitative and quantitative phenotypes and allows for both discrete and continuous covariates to detect G × G and G × E interactions in a sample of unrelated individuals.
Our first approach uses the generalized extreme studentized deviate (GESD) algorithm to detect multiple outliers introduced by the compromised nodes; our second approach uses a threshold derived using a time transformation technique to filter out the outliers.
This identification approach utilizes the generalized form of a Padé Legendre approximation for studying multivariable functions.
This approach uses the generalized logistic equation (GLE) that has an adequate mathematical structure that suits the dynamic characteristic of the crystal growth.
The present approach introduces the generalized high dimensional model representation (GHDMR) wherein diffeomorphic modulation under observable response preserving homotopy (D-MORPH) regression is utilized to ensure the hierarchical orthogonality of high dimensional model representation component functions.
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