globalchange  > 气候变化事实与影响
DOI: 10.1016/j.atmosenv.2014.11.049
Scopus记录号: 2-s2.0-84912002566
论文题名:
Imputation of missing data in time series for air pollutants
作者: Junger W; L; , Ponce de Leon A
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2015
卷: 102
起始页码: 96
结束页码: 104
语种: 英语
英文关键词: Air pollution ; Data imputation ; EM algorithm ; Environmental epidemiology ; Missing data ; Particulate matter ; Time series
Scopus关键词: Air pollution ; Algorithms ; Normal distribution ; Data imputation ; EM algorithms ; Environmental epidemiology ; Missing data ; Particulate Matter ; Time series ; algorithm ; atmospheric pollution ; health impact ; multivariate analysis ; performance assessment ; precision ; software ; time series ; air monitoring ; air pollutant ; air quality ; Article ; chemical composition ; imputation ; measurement accuracy ; prediction ; reference value ; seasonal variation ; simulation ; statistical analysis ; telemetry ; time series analysis
Scopus学科分类: Environmental Science: Water Science and Technology ; Earth and Planetary Sciences: Earth-Surface Processes ; Environmental Science: Environmental Chemistry
英文摘要: Missing data are major concerns in epidemiological studies of the health effects of environmental air pollutants. This article presents an imputation-based method that is suitable for multivariate time series data, which uses the EM algorithm under the assumption of normal distribution. Different approaches are considered for filtering the temporal component. A simulation study was performed to assess validity and performance of proposed method in comparison with some frequently used methods. Simulations showed that when the amount of missing data was as low as 5%, the complete data analysis yielded satisfactory results regardless of the generating mechanism of the missing data, whereas the validity began to degenerate when the proportion of missing values exceeded 10%. The proposed imputation method exhibited good accuracy and precision in different settings with respect to the patterns of missing observations. Most of the imputations obtained valid results, even under missing not at random. The methods proposed in this study are implemented as a package called mtsdi for the statistical software system R. © 2014 Elsevier Ltd.
Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/82036
Appears in Collections:气候变化事实与影响

Files in This Item:

There are no files associated with this item.


作者单位: Rio de Janeiro State University, Department of Epidemiology, Brazil

Recommended Citation:
Junger W,L,, Ponce de Leon A. Imputation of missing data in time series for air pollutants[J]. Atmospheric Environment,2015-01-01,102
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[Junger W]'s Articles
[L]'s Articles
[, Ponce de Leon A]'s Articles
百度学术
Similar articles in Baidu Scholar
[Junger W]'s Articles
[L]'s Articles
[, Ponce de Leon A]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[Junger W]‘s Articles
[L]‘s Articles
[, Ponce de Leon A]‘s Articles
Related Copyright Policies
Null
收藏/分享
所有评论 (0)
暂无评论
 

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