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A Study on the Multiple Imputation of Missing Values: Focus on Fine Dust Data
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결측치 다중 대체에 대한 연구: 미세먼지 자료를 중심으로

논문 기본 정보

Type
Academic journal
Author
Jaehyun Kim (서경대학교)
Journal
The Society of Convergence Knowledge The Society of Convergence Knowledge Transactions Vol.8 No.4 KCI Accredited Journals
Published
2020.12
Pages
149 - 156 (8page)

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A Study on the Multiple Imputation of Missing Values: Focus on Fine Dust Data
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Abstract· Keywords

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Big issues of the Fourth Industrial Revolution are big data and artificial intelligence. Various types of data that are automatically created, stored, processed and analyzed from IoT sensors, SNS, and financial transactions are using for accurate prediction . However, data analysis using incomplete data containing missing values results in biased estimates and resulting erroneous analyses. In this study, the missing values mechanism and the missing values pattern were explored and the classical method of replacing missing values and the multiple imputation methods were considered. Empirical studies conducted simulations of 48,192 fine dust (PM10) and ultrafine dust (PM 2.5) time series data containing missing values, varying the number of data sets replaced by the MICE package of R, the statistical language. As a result, the mutiple imputation method using 20 imputed datasets was judged to be appropriate and were compared with the original datasets that removed missing values to show that the parameters of the original datasets were consistent.
#Missing values #Multiple imputation #Big data #Particulate matter(PM) data #MICE

Contents

ABSTRACT
1. 서론
2. 선행 연구와 다중 대체
3. 실증 연구
4. 결론
References

References (13)

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Little, R. J. A. / 2002 / Statistical Analysis with Missing Data / John Wiley & Sons google schola 김연진 / 2019 / 미세먼지 자료에서의 결측치 대체 방법 비교 / 한국빅데이터학회 학회지 4(2):105~110 Crossref Rubin, D. B. / 1976 / Inference and Missing Data / Biometrika 63(3):581~590 google schola Schafer, J. L. / 2002 / Missing Data: Our View of the State of the Art / Psychological Methods 7(2):147~177 google schola Marsh, H. W. / 1998 / Pairwise Deletion for Missing Data in Structural Equation Models: Nonpositive Definite Matrices, Parameter Estimates, Goodness of Fit, and Adjusted Sample Sizes / Structural Equation Modeling 5(1):22~36 google schola

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