.png){kind=icon}
Return
Data-driven local bandwidth selection for additive models with missing data
.png){kind=icon}
PRE
AI
DOI：10.1016/j.amc.2011.05.040.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper deals in the nonparametric estimation of additive models in the presence of missing data in the response variable. Specifically in the case of additive models estimated by the Backfitting algorithm with local polynomial smoothers [1]. Three estimators are presented, one based on the available data and two based on a complete sample from imputation techniques. We also develop a data-driven local bandwidth selector based on a Wild Bootstrap approximation of the mean squared error of the estimators. The performance of the estimators and the local bootstrap bandwidth selection method are explored through simulation experiments. (C) 2011 Elsevier Inc. All rights reserved.
Keywords:
Missing data
Imputation
Wild Bootstrap
Smoothing parameter
Backfitting
Journal
IF:
3.4
Papers:
2.3W
Citations:
3.3W
Organization
Cited Papers
ShareSaveShareSaveShareSaveUp-regulation of Arl4a gene expression by broccoli aqueous extract is associated with improved spermatogenesis in mouse testes.png)
Biomédica
OAAI
BiomédicaIF0
OAAIShareSaveShareSaveMissing covariates in generalized linear models when the missing data mechanism is non-ignorable
OAAI
OAAIShareSaveShareSave
View more