.png){kind=icon}
Return
BACKFITTING AND SMOOTH BACKFITTING FOR ADDITIVE QUANTILE MODELS
.png){kind=icon}
OA
AI
DOI：10.1214/10-AOS808.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, we study the ordinary backfitting and smooth backfitting as methods of fitting additive quantile models. We show that these backfitting quantile estimators are asymptotically equivalent to the corresponding backfitting estimators of the additive components in a specially-designed additive mean regression model. This implies that the theoretical properties of the backfitting quantile estimators are not unlike those of backfitting mean regression estimators. We also assess the finite sample properties of the two backfitting quantile estimators.
Keywords:
Backfitting
nonparametric regression
quantile estimation
additive models
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.7
Papers:
2.8K
Citations:
2.9W
Organization
View more organizations
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSave
ShareSaveShareSaveShareSave View more