Bootstrapping Nonparametric M-Smoothers with Independent Error Terms

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F18%3A10384378" target="_blank" >RIV/00216208:11320/18:10384378 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-319-96941-1" target="_blank" >https://doi.org/10.1007/978-3-319-96941-1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-96941-1" target="_blank" >10.1007/978-3-319-96941-1</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Bootstrapping Nonparametric M-Smoothers with Independent Error Terms

Popis výsledku v původním jazyce

Nonparametric regression approaches are flexible modeling tools in mod- ern statistics. On the other hand, the lack of any parameters makes these approaches more challenging when assessing some statistical inference in these models. This is crucial especially in situations when one needs to perform some statistical tests or to construct some confidence sets. In such cases, it is common to use a bootstrap ap- proximation instead. It is an effective alternative to more straightforward but rather slow plug-in techniques. In this paper we introduce a proper bootstrap algorithm for a robustified versions of the nonparametric estimates, so called M-smoothers, or M-estimates respectively. We distinguish situations for homoscedastic and het- eroscedastic independent error terms and we prove the consistency of the bootstrap approximation under both scenarios. Technical proofs are provided and the finite sample properties are investigated via a simulation study.

Název v anglickém jazyce

Bootstrapping Nonparametric M-Smoothers with Independent Error Terms

Popis výsledku anglicky

Nonparametric regression approaches are flexible modeling tools in mod- ern statistics. On the other hand, the lack of any parameters makes these approaches more challenging when assessing some statistical inference in these models. This is crucial especially in situations when one needs to perform some statistical tests or to construct some confidence sets. In such cases, it is common to use a bootstrap ap- proximation instead. It is an effective alternative to more straightforward but rather slow plug-in techniques. In this paper we introduce a proper bootstrap algorithm for a robustified versions of the nonparametric estimates, so called M-smoothers, or M-estimates respectively. We distinguish situations for homoscedastic and het- eroscedastic independent error terms and we prove the consistency of the bootstrap approximation under both scenarios. Technical proofs are provided and the finite sample properties are investigated via a simulation study.

Druh

C - Kapitola v odborné knize

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

<a href="/cs/project/GBP402%2F12%2FG097" target="_blank" >GBP402/12/G097: DYME-Dynamické modely v ekonomii</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název knihy nebo sborníku

Nonparametric Statistics - 3rd ISNPS, Avignon, France, June 2016

ISBN

978-3-319-96940-4

Počet stran výsledku

15

Strana od-do

1-15

Počet stran knihy

352

Název nakladatele

Springer Nature Switzerland AG

Místo vydání

Springer Nature Switzerland AG

Kód UT WoS kapitoly

—