Detail publikace

Maximum likelihood method for bandwidth selection in kernel conditional density estimate

POKOROVÁ, K., HOROVÁ, I.

Originální název

Maximum likelihood method for bandwidth selection in kernel conditional density estimate

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

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

Autoři

POKOROVÁ, K., HOROVÁ, I.

Vydáno

2. 11. 2019

Nakladatel

Springer Verlag

Místo

Berlin

ISSN

0943-4062

Periodikum

COMPUTATIONAL STATISTICS & DATA ANALYSIS

Ročník

34

Číslo

4

Stát

Spolková republika Německo

Strany od

1871

Strany do

1887

Strany počet

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"
}