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Bandwidth Selection for Kernel Conditional Density Estimation
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TL;DR: In this paper, several bandwidth selection methods are derived ranging from fast rules-of-thumb which assume the underlying densities are known to relatively slow procedures which use the bootstrap, and a practical bandwidth selection strategy which combines the methods is proposed.
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Abstract: We consider bandwidth selection for the kernel estimator of conditional density with one explanatory variable. Several bandwidth selection methods are derived ranging from fast rules-of-thumb which assume the underlying densities are known to relatively slow procedures which use the bootstrap. The methods are compared and a practical bandwidth selection strategy which combines the methods is proposed. The methods are compared using two simulation studies and a real data set.
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Citations
Kernel density estimators for gaussian mixture models
Tomas Ruzgas,Indrė Drulytė +1 more
- 20 Dec 2013
TL;DR: The adaptive kernel method outperforms the smoothing with a fixed bandwidth in the majority of models and shows better performance for Gaussian mixtures with considerably overlapping components and multiple peaks (double claw distribution).
Assessment and comparison of likely density distributions in the cases of thickness measurement of skin tumours by ultrasound examination and histological analysis
TL;DR: It is showed that thicknesses of the skin tumours measured by ultrasonic method are strongly similar to histological values, which means that the density of ultrasonic thicknesses distribution and density of Normal distribution are closely interconnected.