KERNEL DENSITY ESTIMATORS FOR GAUSSIAN MIXTURE MODELS

@inproceedings{Ruzgas2013KERNELDE,
  title={KERNEL DENSITY ESTIMATORS FOR GAUSSIAN MIXTURE MODELS},
  author={Tomas Ruzgas and Indre Drulyte},
  year={2013}
}
The problem of nonparametric estimation of probability density function is considered. The performance of kernel estimators based on various common kernels and a new kernel K (see (14)) with both fixed and adaptive smoothing bandwidth is compared in terms of the symmetric mean absolute percentage error using the Monte Carlo method. The kernel K is everywhere positive but has lighter tails than the Gaussian density. Gaussian mixture models from a collection introduced by Marron and Wand (1992… CONTINUE READING

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