Bootstrapping Nonparametric M-Smoothers with Independent Error Terms
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Bootstrapping Nonparametric M-Smoothers with Independent Error Terms
Original language description
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.
Czech name
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Czech description
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Classification
Type
C - Chapter in a specialist book
CEP classification
—
OECD FORD branch
10102 - Applied mathematics
Result continuities
Project
<a href="/en/project/GBP402%2F12%2FG097" target="_blank" >GBP402/12/G097: DYME-Dynamic Models in Economics</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Book/collection name
Nonparametric Statistics - 3rd ISNPS, Avignon, France, June 2016
ISBN
978-3-319-96940-4
Number of pages of the result
15
Pages from-to
1-15
Number of pages of the book
352
Publisher name
Springer Nature Switzerland AG
Place of publication
Springer Nature Switzerland AG
UT code for WoS chapter
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