TomAlberts.com

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The focus of this research was to construct non- and semi-parametric estimates of probability density functions based on a random sample of data. The specific goal was to correct negative boundary effects that occur in kernel density estimation for densities with support on only the positive half of the real line. We showed through simulations that our semi-parametric estimator performed well on densities with zero derivative at the endpoint, and that our non-parametric estimators, which combined elements of transformation and reflection, produced excellent results on virtually all shapes of densities.

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Brooks
Canada t1r1b6
+1.4033623977

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