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Patches of attacked trees were identified using a kernel estimation procedure coupled with randomization tests.
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First, crash clusters were investigated using a kernel density estimation (KDE) approach.
In this work, we propose using a Kernel Density Estimation (KDE) of the concentrations that allows getting the expected results for a well-mixed solution with a limited number of particles.
This scenario is demonstrated in the "Results" section, using a kernel density estimation (KDE) AD method.
Calculating the probability from a set of samples is typically done by using a kernel density estimation (KDE) procedure [22], also known as a Parzen window method [23].
The areas that were at a higher risk of dengue occurrence were detected using a kernel density estimation with the kernel quartic function.
Using the gene set enrichment scores obtained via ssGSEA, Constellation Map estimates the probability density functions of gene set and phenotypic class variables using a kernel density estimation.
We also analyzed the distribution of markers along the chromosomes, by using a kernel density estimation to calculate optimal window size (bandwidth) for dividing the genome into blocks, in which we counted the number of genes.
This approach is similar to the approach in [ 28, 32, 71], where a fixation- density map is calculated using a kernel-density estimation with Gaussian kernels.
Patient trajectories are shown together with the estimated mean function (bold curve) obtained using a kernel smoother (a nonparametric function estimation procedure) with bandwidth chosen via the generalized cross-validation (GVC) procedure implemented in PACE package in Matlab.
CGM data was analyzed to find optimal values of parameters by using ordinary least squares fitting or maximum likelihood estimation using a kernel-density approximation.
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CEO of Professional Science Editing for Scientists @ prosciediting.com