Detail publikace

Priestley-Chao Estimator of Conditional Density

KONEČNÁ, K.

Originální název

Priestley-Chao Estimator of Conditional Density

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

This contribution is focused on a non-parametric estimation of conditional density. Several types of kernel estimators of conditional density are known, the Nadaraya-Watson and the local linear estimators are the widest used ones. We focus on a new estimator - the Priestley-Chao estimator of conditional density. As conditional density can be regarded as a generalization of regression, the Priestley-Chao estimator, proposed initially for kernel regression, is extended for kernel estimation of conditional density. The conditional characteristics and the statistical properties of the suggested estimator are derived. The estimator depends on the smoothing parameters called bandwidths which influence the final quality of the estimate significantly. The cross-validation method is suggested for their estimation and the expression for the cross-validation function is derived. The theoretical approach is supplemented by a simulation study.

Klíčová slova

kernel smoothing; conditional density; Priestley-Chao estimator; statistical properties; bandwidth selection; cross-validation method

Autoři

KONEČNÁ, K.

Vydáno

1. 12. 2017

Nakladatel

University of Defence, Brno, 2017

Místo

Brno

ISBN

978-80-7582-026-6

Kniha

Mathematics, Information Technologies and Applied Sciences 2017, post-conference proceedings of extended versions of selected papers

Strany od

151

Strany do

163

Strany počet

13

URL

http://mitav.unob.cz/data/MITAV%202017%20Proceedings.pdf

BibTex

@inproceedings{BUT142655,
  author="Kateřina {Pokorová}",
  title="Priestley-Chao Estimator of Conditional Density",
  booktitle="Mathematics, Information Technologies and Applied Sciences 2017, post-conference proceedings of extended versions of selected papers",
  year="2017",
  pages="151--163",
  publisher="University of Defence, Brno, 2017",
  address="Brno",
  isbn="978-80-7582-026-6",
  url="http://mitav.unob.cz/data/MITAV%202017%20Proceedings.pdf"
}