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Statistical micro matching using a multinomial logistic regression model for categorical data

Authors
Kim, KangminPark, Mingue
Issue Date
9월-2019
Publisher
KOREAN STATISTICAL SOC
Keywords
statistical matching; multinomial logistic regression model; conditional independence assumption; auxiliary information
Citation
COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS, v.26, no.5, pp.507 - 517
Indexed
SCOPUS
KCI
Journal Title
COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS
Volume
26
Number
5
Start Page
507
End Page
517
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/63023
DOI
10.29220/CSAM.2019.26.5.507
ISSN
2287-7843
Abstract
Statistical matching is a method of combining multiple sources of data that are extracted or surveyed from the same population. It can be used in situation when variables of interest are not jointly observed. It is a low-cost way to expect high-effects in terms of being able to create synthetic data using existing sources. In this paper, we propose the several statistical micro matching methods using a multinomial logistic regression model when all variables of interest are categorical or categorized ones, which is common in sample survey. Under conditional independence assumption (CIA), a mixed statistical matching method, which is useful when auxiliary information is not available, is proposed. We also propose a statistical matching method with auxiliary information that reduces the bias of the conventional matching methods suggested under CIA. Through a simulation study, proposed micro matching methods and conventional ones are compared. Simulation study shows that suggested matching methods outperform the existing ones especially when CIA does not hold.
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