Kernel density estimation for directional-linear data

@article{GarcaPortugus2013KernelDE,
  title={Kernel density estimation for directional-linear data},
  author={E. Garc{\'i}a-Portugu{\'e}s and R. Crujeiras and W. Gonz{\'a}lez-Manteiga},
  journal={J. Multivar. Anal.},
  year={2013},
  volume={121},
  pages={152-175}
}
  • E. García-Portugués, R. Crujeiras, W. González-Manteiga
  • Published 2013
  • Computer Science, Mathematics
  • J. Multivar. Anal.
  • A nonparametric kernel density estimator for directional-linear data is introduced. The proposal is based on a product kernel accounting for the different nature of both (directional and linear) components of the random vector. Expressions for the bias, variance, and mean integrated square error (MISE) are derived, jointly with an asymptotic normality result for the proposed estimator. For some particular distributions, an explicit formula for the MISE is obtained and compared with its… CONTINUE READING

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