Priestley-Chao Estimator of Conditional Density

D 2017

Priestley-Chao Estimator of Conditional Density

KONEČNÁ, Kateřina

Basic information

Original name

Priestley-Chao Estimator of Conditional Density

Authors

KONEČNÁ, Kateřina (203 Czech Republic, guarantor, belonging to the institution)

Edition

Brno, Mathematics, Information Technologies and Applied Sciences 2017, post-conference proceedings of extended versions of selected papers, p. 151-163, 13 pp. 2017

Publisher

University of Defence, Brno, 2017

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10103 Statistics and probability

Country of publisher

Czech Republic

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

References:

URL

RIV identification code

RIV/00216224:14310/17:00095286

Organization unit

Faculty of Science

ISBN

978-80-7582-026-6

Keywords in English

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

Abstract

V originále

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.

Links

GA15-06991S, research and development project

Name: Analýza funkcionálních dat a související témata

Investor: Czech Science Foundation

Citovat

KONEČNÁ, Kateřina. Priestley-Chao Estimator of Conditional Density. Online. In Jaromír Baštinec, Miroslav Hrubý. Mathematics, Information Technologies and Applied Sciences 2017, post-conference proceedings of extended versions of selected papers. Brno: University of Defence, Brno, 2017, 2017, p. 151-163. ISBN 978-80-7582-026-6.

@inproceedings{1400340,
   author = {Konečná, Kateřina},
   address = {Brno},
   booktitle = {Mathematics, Information Technologies and Applied Sciences 2017, post-conference proceedings of extended versions of selected papers},
   editor = {Jaromír Baštinec, Miroslav Hrubý},
   keywords = {kernel smoothing; conditional density; Priestley-Chao estimator; statistical properties; bandwidth selection; cross-validation method},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Brno},
   isbn = {978-80-7582-026-6},
   pages = {151-163},
   publisher = {University of Defence, Brno, 2017},
   title = {Priestley-Chao Estimator of Conditional Density},
   url = {http://mitav.unob.cz/data/MITAV%202017%20Proceedings.pdf},
   year = {2017}
}

TY  - JOUR
ID  - 1400340
AU  - Konečná, Kateřina
PY  - 2017
TI  - Priestley-Chao Estimator of Conditional Density
PB  - University of Defence, Brno, 2017
CY  - Brno
SN  - 9788075820266
KW  - kernel smoothing
KW  - conditional density
KW  - Priestley-Chao estimator
KW  - statistical properties
KW  - bandwidth selection
KW  - cross-validation method
UR  - http://mitav.unob.cz/data/MITAV%202017%20Proceedings.pdf
N2  - 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.
ER  -

KONEČNÁ, Kateřina. Priestley-Chao Estimator of Conditional Density. Online. In Jaromír Baštinec, Miroslav Hrubý. \textit{Mathematics, Information Technologies and Applied Sciences 2017, post-conference proceedings of extended versions of selected papers}. Brno: University of Defence, Brno, 2017, 2017, p.~151-163. ISBN~978-80-7582-026-6.

Detailed Information on Publication Record