BibTeX
CSL-JSON
MLA
Harvard
The Local LinearM-Estimation with Missing Response Data
release_xkrwossrm5dxhm6wg25omcb7jy
by
Shuanghua Luo, Cheng-Yi Zhang, Fengmin Xu
Abstract
This paper studies the nonparametric regressive function with missing response data. Three local linear<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:mi>M</mml:mi></mml:mrow></mml:math>-estimators with the robustness of local linear regression smoothers are presented such that they have the same asymptotic normality and consistency. Then finite-sample performance is examined via simulation studies. Simulations demonstrate that the complete-case data<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mrow><mml:mi>M</mml:mi></mml:mrow></mml:math>-estimator is not superior to the other two local linear<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mml:mrow><mml:mi>M</mml:mi></mml:mrow></mml:math>-estimators.
In application/xml+jats format
Archived Files and Locations
application/pdf 2.1 MB
file_qxhvmmujuvcbfktifaykgbuhwq
|
web.archive.org (webarchive) pdfs.semanticscholar.org (aggregator) |
application/pdf 687.3 kB
file_zljnfxrv6rhi7izixee54k5oq4
|
projecteuclid.org (web) web.archive.org (webarchive) |
Read Archived PDF
Preserved and Accessible
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
access all versions, variants, and formats of this works (eg, pre-prints)
Cite This
Lookup Links
oaDOI/unpaywall (OA fulltext)
Crossref Metadata (via API)
Worldcat
SHERPA/RoMEO (journal policies)
wikidata.org
CORE.ac.uk
Semantic Scholar
Google Scholar
Crossref Metadata (via API)
Worldcat
SHERPA/RoMEO (journal policies)
wikidata.org
CORE.ac.uk
Semantic Scholar
Google Scholar