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  • Kernel density estimates belong to the most popular nonparametric density estimates. It is a known fact, that these estimates depend on a bandwidth, which controls the smoothness of the estimate, and on a kernel, which plays a role of a weight function. Methods for finding the optimal bandwidth were presented e.g. in Duong & Hazelton (2005), Sain et al. (1994). We focus on the kernel function choice, especially on kernels with a bounded support. Wand and Jones (1995) suggested a criterion for the optimal kernel. Our aim is to study the optimality of the kernel with respect to the bandwidth choice. A simulation study brings comparison of the kernels. It shows up, that the cosine kernel performs better than the frequently used Epanechnikov kernel.
  • Kernel density estimates belong to the most popular nonparametric density estimates. It is a known fact, that these estimates depend on a bandwidth, which controls the smoothness of the estimate, and on a kernel, which plays a role of a weight function. Methods for finding the optimal bandwidth were presented e.g. in Duong & Hazelton (2005), Sain et al. (1994). We focus on the kernel function choice, especially on kernels with a bounded support. Wand and Jones (1995) suggested a criterion for the optimal kernel. Our aim is to study the optimality of the kernel with respect to the bandwidth choice. A simulation study brings comparison of the kernels. It shows up, that the cosine kernel performs better than the frequently used Epanechnikov kernel. (en)
Title
  • Kernel choosing with respect to the bandwidth in kernel density estimates
  • Kernel choosing with respect to the bandwidth in kernel density estimates (en)
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  • Kernel choosing with respect to the bandwidth in kernel density estimates
  • Kernel choosing with respect to the bandwidth in kernel density estimates (en)
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  • RIV/00216224:14750/10:00044521!RIV11-MSM-14750___
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  • RIV/00216224:14750/10:00044521
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  • kernel; bounded support; density estimation (en)
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  • [CE6442B45711]
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  • Vopatová, Kamila
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  • 14750
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