Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Robust multiple imputation method for missings with boundary and outliers

Authors
Park, YousungOh, Do YoungKwon, Tae Yeon
Issue Date
12월-2019
Publisher
KOREAN STATISTICAL SOC
Keywords
break-down point; robust regression; Bayesian multiple imputation
Citation
KOREAN JOURNAL OF APPLIED STATISTICS, v.32, no.6, pp.889 - 898
Indexed
KCI
Journal Title
KOREAN JOURNAL OF APPLIED STATISTICS
Volume
32
Number
6
Start Page
889
End Page
898
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/61444
DOI
10.5351/KJAS.2019.32.6.889
ISSN
1225-066X
Abstract
The problem of missing value imputation for variables in surveys that include item missing becomes complicated if outliers and logical boundary conditions between other survey items cannot be ignored. If there are outliers and boundaries in a variable including missing values, imputed values based on previous regression-based imputation methods are likely to be biased and not meet boundary conditions. In this paper, we approach these difficulties in imputation by combining various robust regression models and multiple imputation methods. Through a simulation study on various scenarios of outliers and boundaries, we find and discuss the optimal combination of robust regression and multiple imputation method.
Files in This Item
There are no files associated with this item.
Appears in
Collections
College of Political Science & Economics > Department of Statistics > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Altmetrics

Total Views & Downloads

BROWSE