A plug-in rule for bandwidth selection in circular density estimation

@article{Oliveira2012APR,
  title={A plug-in rule for bandwidth selection in circular density estimation},
  author={Mar{\'i}a Oliveira and Rosa M. Crujeiras and Alberto Rodr{\'i}guez-Casal},
  journal={Comput. Stat. Data Anal.},
  year={2012},
  volume={56},
  pages={3898-3908}
}

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References

SHOWING 1-10 OF 26 REFERENCES
Automatic bandwidth selection for circular density estimation
  • C. Taylor
  • Mathematics
    Comput. Stat. Data Anal.
  • 2008
Bootstrap choice of the smoothing parameter in kernel density estimation
SUMMARY Cross-validation based on integrated squared error has already been applied to the choice of smoothing parameter in the kernel method of density estimation. In this paper, an alternative
Contribution to the bandwidth choice for kernel density estimates
TLDR
This paper focuses on the problem of the bandwidth choice for the kernel density estimates and the idea of maximal smoothing principle is extended to the higher order kernels.
Relative efficiency of local bandwidths in kernel density estimation*
The concept of relative efficiency can be used in kernel density estimation to analyze the global benefits of the optimal local bandwidth selector with respect to the optima* global one, in terms of
The wrapped skew-normal distribution on the circle
The wrapped skew-normal distribution is proposed as a model for circular data. Basic results for the distribution are established and estimation for a circular parametrisation of it considered.
Kernel density estimation with spherical data
SUMMARY We study two natural classes of kernel density estimators for use with spherical data. Members of both classes have already been used in practice. The classes have an element in common, but
Nonparametric circular methods for exploring environmental data
The goal of this work is to introduce nonparametric kernel methods for density and regression estimation for circular data, and illustrate their use by a brief simulation study and real data
...
...