QQ客服 官方微博 反馈留言

	Missing data analysis: A kernel-based multi-imputation approach
	Zhang, Shichao ; Jin, Zhi ; Zhu, Xiaofeng ; Zhang, Jilian
	2009
英文摘要	Many missing data analysis techniques are of single-imputation. However, single-imputation cannot provide valid standard errors and confidence intervals, since it ignores the uncertainty implicit in the fact that the imputed values are not the actual values. Filling in each missing value with a set of plausible values is called multi-imputation. In this paper we propose a kernel- based stochastic non-parametric multi-imputation method under MAR (Missing at Random) and MCAR (Missing Completely at Random) missing mechanisms in nonparametric regression settings. Furthermore, we present a kernel-based stochastic semi-parametric multi-imputation method while we have some priori knowledge about the dataset with missing. Our algorithms are designed specifically with the aim of optimizing the confidence-interval and the relative efficiency. The proposed technique is evaluated by experimentations, using simulation data and real data, and the results demonstrate that our method performs much better than the NORM method, and is promising. ? Springer-Verlag Berlin Heidelberg 2009.; EI; 0
语种	英语
DOI标识	10.1007/978-3-642-00212-0_7
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/327504]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Zhang, Shichao,Jin, Zhi,Zhu, Xiaofeng,et al. Missing data analysis: A kernel-based multi-imputation approach. 2009-01-01.

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。