Multiple Imputation for Multivariate Data with Missing and Below‐Threshold Measurements: Time‐Series Concentrations of Pollutants in the Arctic
Summary. Many chemical and environmental data sets are complicated by the existence of fully missing values or censored values known to lie below detection thresholds. For example, week‐long samples of airborne particulate matter were obtained at Alert, NWT, Canada, between 1980 and 1991, where some...
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crwiley:10.1111/j.0006-341x.2001.00022.x 2023-12-03T10:17:56+01:00 Multiple Imputation for Multivariate Data with Missing and Below‐Threshold Measurements: Time‐Series Concentrations of Pollutants in the Arctic Hopke, Philip K. Liu, Chuanhai Rubin, Donald B. 2001 http://dx.doi.org/10.1111/j.0006-341x.2001.00022.x https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fj.0006-341X.2001.00022.x https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.0006-341X.2001.00022.x en eng Wiley http://onlinelibrary.wiley.com/termsAndConditions#vor Biometrics volume 57, issue 1, page 22-33 ISSN 0006-341X 1541-0420 Applied Mathematics General Agricultural and Biological Sciences General Immunology and Microbiology General Biochemistry, Genetics and Molecular Biology General Medicine Statistics and Probability journal-article 2001 crwiley https://doi.org/10.1111/j.0006-341x.2001.00022.x 2023-11-09T14:37:37Z Summary. Many chemical and environmental data sets are complicated by the existence of fully missing values or censored values known to lie below detection thresholds. For example, week‐long samples of airborne particulate matter were obtained at Alert, NWT, Canada, between 1980 and 1991, where some of the concentrations of 24 particulate constituents were coarsened in the sense of being either fully missing or below detection limits. To facilitate scientific analysis, it is appealing to create complete data by filling in missing values so that standard complete‐data methods can be applied. We briefly review commonly used strategies for handling missing values and focus on the multiple‐imputation approach, which generally leads to valid inferences when faced with missing data. Three statistical models are developed for multiply imputing the missing values of airborne particulate matter. We expect that these models are useful for creating multiple imputations in a variety of incomplete multivariate time series data sets. Article in Journal/Newspaper Arctic Wiley Online Library (via Crossref) Arctic Canada Biometrics 57 1 22 33 |
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Open Polar |
collection |
Wiley Online Library (via Crossref) |
op_collection_id |
crwiley |
language |
English |
topic |
Applied Mathematics General Agricultural and Biological Sciences General Immunology and Microbiology General Biochemistry, Genetics and Molecular Biology General Medicine Statistics and Probability |
spellingShingle |
Applied Mathematics General Agricultural and Biological Sciences General Immunology and Microbiology General Biochemistry, Genetics and Molecular Biology General Medicine Statistics and Probability Hopke, Philip K. Liu, Chuanhai Rubin, Donald B. Multiple Imputation for Multivariate Data with Missing and Below‐Threshold Measurements: Time‐Series Concentrations of Pollutants in the Arctic |
topic_facet |
Applied Mathematics General Agricultural and Biological Sciences General Immunology and Microbiology General Biochemistry, Genetics and Molecular Biology General Medicine Statistics and Probability |
description |
Summary. Many chemical and environmental data sets are complicated by the existence of fully missing values or censored values known to lie below detection thresholds. For example, week‐long samples of airborne particulate matter were obtained at Alert, NWT, Canada, between 1980 and 1991, where some of the concentrations of 24 particulate constituents were coarsened in the sense of being either fully missing or below detection limits. To facilitate scientific analysis, it is appealing to create complete data by filling in missing values so that standard complete‐data methods can be applied. We briefly review commonly used strategies for handling missing values and focus on the multiple‐imputation approach, which generally leads to valid inferences when faced with missing data. Three statistical models are developed for multiply imputing the missing values of airborne particulate matter. We expect that these models are useful for creating multiple imputations in a variety of incomplete multivariate time series data sets. |
format |
Article in Journal/Newspaper |
author |
Hopke, Philip K. Liu, Chuanhai Rubin, Donald B. |
author_facet |
Hopke, Philip K. Liu, Chuanhai Rubin, Donald B. |
author_sort |
Hopke, Philip K. |
title |
Multiple Imputation for Multivariate Data with Missing and Below‐Threshold Measurements: Time‐Series Concentrations of Pollutants in the Arctic |
title_short |
Multiple Imputation for Multivariate Data with Missing and Below‐Threshold Measurements: Time‐Series Concentrations of Pollutants in the Arctic |
title_full |
Multiple Imputation for Multivariate Data with Missing and Below‐Threshold Measurements: Time‐Series Concentrations of Pollutants in the Arctic |
title_fullStr |
Multiple Imputation for Multivariate Data with Missing and Below‐Threshold Measurements: Time‐Series Concentrations of Pollutants in the Arctic |
title_full_unstemmed |
Multiple Imputation for Multivariate Data with Missing and Below‐Threshold Measurements: Time‐Series Concentrations of Pollutants in the Arctic |
title_sort |
multiple imputation for multivariate data with missing and below‐threshold measurements: time‐series concentrations of pollutants in the arctic |
publisher |
Wiley |
publishDate |
2001 |
url |
http://dx.doi.org/10.1111/j.0006-341x.2001.00022.x https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fj.0006-341X.2001.00022.x https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.0006-341X.2001.00022.x |
geographic |
Arctic Canada |
geographic_facet |
Arctic Canada |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Biometrics volume 57, issue 1, page 22-33 ISSN 0006-341X 1541-0420 |
op_rights |
http://onlinelibrary.wiley.com/termsAndConditions#vor |
op_doi |
https://doi.org/10.1111/j.0006-341x.2001.00022.x |
container_title |
Biometrics |
container_volume |
57 |
container_issue |
1 |
container_start_page |
22 |
op_container_end_page |
33 |
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1784264901333614592 |