Maximum likelihood method for bandwidth selection in kernel conditional density estimate

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F19%3APU134070" target="_blank" >RIV/00216305:26110/19:PU134070 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216224:14310/19:00111007

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s00180-019-00884-0" target="_blank" >https://link.springer.com/article/10.1007/s00180-019-00884-0</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00180-019-00884-0" target="_blank" >10.1007/s00180-019-00884-0</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Maximum likelihood method for bandwidth selection in kernel conditional density estimate

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Maximum likelihood method for bandwidth selection in kernel conditional density estimate

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

COMPUTATIONAL STATISTICS & DATA ANALYSIS

ISSN

0943-4062

e-ISSN

1613-9658

Svazek periodika

34

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

16

Strana od-do

1871-1887

Kód UT WoS článku

000501848900019

EID výsledku v databázi Scopus

2-s2.0-85064158959