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POKOROVÁ, K., HOROVÁ, I.
Original Title
Maximum likelihood method for bandwidth selection in kernel conditional density estimate
Type
journal article in Web of Science
Language
English
Original 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.
Keywords
kernel smoothing; conditional density; methods for bandwidth selection; leave-one-out maximum likelihood method
Authors
Released
2. 11. 2019
Publisher
Springer Verlag
Location
Berlin
ISBN
0943-4062
Periodical
COMPUTATIONAL STATISTICS & DATA ANALYSIS
Year of study
34
Number
4
State
Federal Republic of Germany
Pages from
1871
Pages to
1887
Pages count
16
URL
https://link.springer.com/article/10.1007/s00180-019-00884-0
BibTex
@article{BUT159825, author="Kateřina {Pokorová} and Ivanka {Horová}", title="Maximum likelihood method for bandwidth selection in kernel conditional density estimate", journal="COMPUTATIONAL STATISTICS & DATA ANALYSIS", year="2019", volume="34", number="4", pages="1871--1887", doi="10.1007/s00180-019-00884-0", issn="0943-4062", url="https://link.springer.com/article/10.1007/s00180-019-00884-0" }