.png){kind=icon}
返回
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}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
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.
Keyword:
Missing data
Imputation
Wild Bootstrap
Smoothing parameter
Backfitting
期刊
IF:
3.4
论文数:
2.3W
被引数:
3.3W
机构
引用论文
分享收藏Up-regulation of Arl4a gene expression by broccoli aqueous extract is associated with improved spermatogenesis in mouse testes西兰花水提物上调Arl4a基因表达与小鼠睾丸中改善的精子发生相关.png)
Biomédica
OAAI
BiomédicaIF0
OAAI分享收藏分享收藏Missing covariates in generalized linear models when the missing data mechanism is non-ignorable
OAAI
OAAI分享收藏分享收藏
查看更多内容