Priestley-Chao Estimator of Conditional Density

D 2017

Priestley-Chao Estimator of Conditional Density

KONEČNÁ, Kateřina

Základní údaje

Originální název

Priestley-Chao Estimator of Conditional Density

Autoři

KONEČNÁ, Kateřina (203 Česká republika, garant, domácí)

Vydání

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

Nakladatel

University of Defence, Brno, 2017

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10103 Statistics and probability

Stát vydavatele

Česká republika

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Odkazy

URL

Kód RIV

RIV/00216224:14310/17:00095286

Organizační jednotka

Přírodovědecká fakulta

ISBN

978-80-7582-026-6

Klíčová slova anglicky

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

Štítky

NZ, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 19. 3. 2018 09:40, Mgr. Kateřina Pokorová, Ph.D.

Anotace

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.

Návaznosti

GA15-06991S, projekt VaV

Název: Analýza funkcionálních dat a související témata

Investor: Grantová agentura ČR, Analýza funkcionálních dat a související témata

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, s. 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, s.~151-163. ISBN~978-80-7582-026-6.

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