A Novel Nonparametric Density Estimator


Autoria(s): Botev, Z. I.
Data(s)

12/11/2006

Resumo

We present a novel nonparametric density estimator and a new data-driven bandwidth selection method with excellent properties. The approach is in- spired by the principles of the generalized cross entropy method. The pro- posed density estimation procedure has numerous advantages over the tra- ditional kernel density estimator methods. Firstly, for the first time in the nonparametric literature, the proposed estimator allows for a genuine incor- poration of prior information in the density estimation procedure. Secondly, the approach provides the first data-driven bandwidth selection method that is guaranteed to provide a unique bandwidth for any data. Lastly, simulation examples suggest the proposed approach outperforms the current state of the art in nonparametric density estimation in terms of accuracy and reliability.

Identificador

http://espace.library.uq.edu.au/view/UQ:12535/Nonparametric_estimation.pdf

http://espace.library.uq.edu.au/view/UQ:12535

Palavras-Chave #nonparametric density estimation #statistics #230000 Mathematical Sciences
Tipo

Seminar Paper