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Two estimation methods for SNP-based heritability were compared, one based on the distant genetic relatedness among all subjects as summarized in a Genetic Relatedness Matrix (GRM), and the other one based on density estimation (DE).
Previous methods based on density estimation required high computational complexity.
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In this work, a non-parametric method for probability density function estimation based on kernel density estimation [7] is used.
It thus makes sense that density affects numerosity estimation, even if numerosity estimation is not based on density perception.
We designed a statistical approach based on kernel density estimation to analyse gene density and identified seven gene-rich isochors.
A clustering technique based on kernel density estimation is described that identifies clusters in terms of the design variables.
In this paper, a new statistical method, based on kernel density estimation, is used for analyzing fractal coding parameters.
Nearest local maximum searching algorithm (NLMSA), an unsupervised clustering algorithm based on kernel density estimation, is proposed.
Entropy is computed locally, on portions of a genuine signature, based on local density estimation by a Client-Hidden Markov Model.
On the other hand, our outlier removal algorithm is based on kernel density estimation and is conducted on 3D unorganized points instead of the 2D image space of [4].
Therefore a minimum reserve size was calculated based on our density estimation in TWR.
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