POKOROVÁ, Kateřina and Ivanka HOROVÁ. Maximum likelihood method for bandwidth selection in kernel conditional density estimate. Computational Statistics. Heidelberg: Springer Heidelberg, 2019, vol. 34, No 4, p. 1871-1887. ISSN 0943-4062. Available from: https://dx.doi.org/10.1007/s00180-019-00884-0.
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Basic information
Original name Maximum likelihood method for bandwidth selection in kernel conditional density estimate
Authors POKOROVÁ, Kateřina (203 Czech Republic, belonging to the institution) and Ivanka HOROVÁ (203 Czech Republic, belonging to the institution).
Edition Computational Statistics, Heidelberg, Springer Heidelberg, 2019, 0943-4062.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 10103 Statistics and probability
Country of publisher Germany
Confidentiality degree is not subject to a state or trade secret
WWW Full Text
Impact factor Impact factor: 0.744
RIV identification code RIV/00216224:14310/19:00111007
Organization unit Faculty of Science
Doi http://dx.doi.org/10.1007/s00180-019-00884-0
UT WoS 000501848900019
Keywords (in Czech) jádrové vyhlazování; podmíněná hustota; metody odhadu vyhlazovacích parametrů; leave-one-out metoda maximální věrohodnosti
Keywords in English kernel smoothing; conditional density; methods for bandwidth selection; leave-one-out maximum likelihood method
Tags rivok
Tags International impact, Reviewed
Changed by Changed by: Mgr. Marie Šípková, DiS., učo 437722. Changed: 12/2/2020 11:17.
Abstract
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.
Links
MUNI/A/1503/2018, interní kód MUName: Matematické statistické modelování 3 (Acronym: MaStaMo3)
Investor: Masaryk University, Category A
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