Maximum likelihood method for bandwidth selection in kernel
conditional density estimate

J 2019

Maximum likelihood method for bandwidth selection in kernel conditional density estimate

POKOROVÁ, Kateřina a Ivanka HOROVÁ

Základní údaje

Originální název

Maximum likelihood method for bandwidth selection in kernel conditional density estimate

Autoři

POKOROVÁ, Kateřina (203 Česká republika, domácí) a Ivanka HOROVÁ (203 Česká republika, domácí)

Vydání

Computational Statistics, Heidelberg, Springer Heidelberg, 2019, 0943-4062

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10103 Statistics and probability

Stát vydavatele

Německo

Utajení

není předmětem státního či obchodního tajemství

Odkazy

Full Text

Impakt faktor

Impact factor: 0.744

Kód RIV

RIV/00216224:14310/19:00111007

Organizační jednotka

Přírodovědecká fakulta

DOI

http://dx.doi.org/10.1007/s00180-019-00884-0

UT WoS

000501848900019

EID Scopus

2-s2.0-85064158959

Klíčová slova česky

jádrové vyhlazování; podmíněná hustota; metody odhadu vyhlazovacích parametrů; leave-one-out metoda maximální věrohodnosti

Klíčová slova anglicky

kernel smoothing; conditional density; methods for bandwidth selection; leave-one-out maximum likelihood method

Štítky

rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 12. 2. 2020 11:17, Mgr. Marie Novosadová Šípková, DiS.

Anotace

V originále

This paper discusses the kernel estimator of conditional density. A significant problem of kernel smoothing is bandwidth selection. The problem consists in the fact that optimal bandwidth depends on the unknown conditional and marginal density. This is the reason why some data-driven method needs to be applied. In this paper, we suggest a method for bandwidth selection based on a classical maximum likelihood approach. We consider a slight modification of the original method—the maximum likelihood method with one observation being left out. Applied to two types of conditional density estimators—to the Nadaraya–Watson and local linear estimator, the proposed method is compared with other known methods in a simulation study. Our aim is to compare the methods from different points of view, concentrating on the accuracy of the estimated bandwidths, on the final model quality measure, and on the computational time.

Návaznosti

MUNI/A/1503/2018, interní kód MU

Název: Matematické statistické modelování 3 (Akronym: MaStaMo3)

Investor: Masarykova univerzita, Matematické statistické modelování 3, DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty

Citovat

POKOROVÁ, Kateřina a Ivanka HOROVÁ. Maximum likelihood method for bandwidth selection in kernel conditional density estimate. Computational Statistics. Heidelberg: Springer Heidelberg, 2019, roč. 34, č. 4, s. 1871-1887. ISSN 0943-4062. Dostupné z: https://dx.doi.org/10.1007/s00180-019-00884-0.

@article{1571859,
   author = {Pokorová, Kateřina and Horová, Ivanka},
   article_location = {Heidelberg},
   article_number = {4},
   doi = {http://dx.doi.org/10.1007/s00180-019-00884-0},
   keywords = {kernel smoothing; conditional density; methods for bandwidth selection; leave-one-out maximum likelihood method},
   language = {eng},
   issn = {0943-4062},
   journal = {Computational Statistics},
   title = {Maximum likelihood method for bandwidth selection in kernel conditional density estimate},
   url = {https://link.springer.com/article/10.1007/s00180-019-00884-0},
   volume = {34},
   year = {2019}
}

TY  - JOUR
ID  - 1571859
AU  - Pokorová, Kateřina - Horová, Ivanka
PY  - 2019
TI  - Maximum likelihood method for bandwidth selection in kernel conditional density estimate
JF  - Computational Statistics
VL  - 34
IS  - 4
SP  - 1871-1887
EP  - 1871-1887
PB  - Springer Heidelberg
SN  - 09434062
KW  - kernel smoothing
KW  - conditional density
KW  - methods for bandwidth selection
KW  - leave-one-out maximum likelihood method
UR  - https://link.springer.com/article/10.1007/s00180-019-00884-0
L2  - https://link.springer.com/article/10.1007/s00180-019-00884-0
N2  - This paper discusses the kernel estimator of conditional density. A significant problem of kernel smoothing is bandwidth selection. The problem consists in the fact that optimal bandwidth depends on the unknown conditional and marginal density. This is the reason why some data-driven method needs to be applied. In this paper, we suggest a method for bandwidth selection based on a classical maximum likelihood approach. We consider a slight modification of the original method—the maximum likelihood method with one observation being left out. Applied to two types of conditional density estimators—to the Nadaraya–Watson and local linear estimator, the proposed method is compared with other known methods in a simulation study. Our aim is to compare the methods from different points of view, concentrating on the accuracy of the estimated bandwidths, on the final model quality measure, and on the computational time.
ER  -

POKOROVÁ, Kateřina a Ivanka HOROVÁ. Maximum likelihood method for bandwidth selection in kernel conditional density estimate. \textit{Computational Statistics}. Heidelberg: Springer Heidelberg, 2019, roč.~34, č.~4, s.~1871-1887. ISSN~0943-4062. Dostupné z: https://dx.doi.org/10.1007/s00180-019-00884-0.

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