Multiple imputation of maritime search and rescue data at multiple missing patterns.

Based on the missing situation and actual needs of maritime search and rescue data, multiple imputation methods were used to construct complete data sets under different missing patterns. Probability density curves and overimputation diagnostics were used to explore the effects of multiple imputatio...

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Autores principales:	Guobo Wang, Minglu Ma, Lili Jiang, Fengyun Chen, Liansheng Xu
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e3e52005007544e891a447859b89ec56
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spelling	oai:doaj.org-article:e3e52005007544e891a447859b89ec562021-12-02T20:05:19ZMultiple imputation of maritime search and rescue data at multiple missing patterns.1932-620310.1371/journal.pone.0252129https://doaj.org/article/e3e52005007544e891a447859b89ec562021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0252129https://doaj.org/toc/1932-6203Based on the missing situation and actual needs of maritime search and rescue data, multiple imputation methods were used to construct complete data sets under different missing patterns. Probability density curves and overimputation diagnostics were used to explore the effects of multiple imputation. The results showed that the Data Augmentation (DA) algorithm had the characteristics of high operation efficiency and good imputation effect, but the algorithm was not suitable for data imputation when there was a high data missing rate. The EMB algorithm effectively restored the distribution of datasets with different data missing rates, and was less affected by the missing position; the EMB algorithm could obtain a good imputation effect even when there was a high data missing rate. Overimputation diagnostics could not only reflect the data imputation effect, but also show the correlation between different datasets, which was of great importance for deep data mining and imputation effect improvement. The Expectation-Maximization with Bootstrap (EMB) algorithm had a poor estimation effect on extreme data and failed to reflect the dataset's variability characteristics.Guobo WangMinglu MaLili JiangFengyun ChenLiansheng XuPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0252129 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Guobo Wang Minglu Ma Lili Jiang Fengyun Chen Liansheng Xu Multiple imputation of maritime search and rescue data at multiple missing patterns.
description	Based on the missing situation and actual needs of maritime search and rescue data, multiple imputation methods were used to construct complete data sets under different missing patterns. Probability density curves and overimputation diagnostics were used to explore the effects of multiple imputation. The results showed that the Data Augmentation (DA) algorithm had the characteristics of high operation efficiency and good imputation effect, but the algorithm was not suitable for data imputation when there was a high data missing rate. The EMB algorithm effectively restored the distribution of datasets with different data missing rates, and was less affected by the missing position; the EMB algorithm could obtain a good imputation effect even when there was a high data missing rate. Overimputation diagnostics could not only reflect the data imputation effect, but also show the correlation between different datasets, which was of great importance for deep data mining and imputation effect improvement. The Expectation-Maximization with Bootstrap (EMB) algorithm had a poor estimation effect on extreme data and failed to reflect the dataset's variability characteristics.
format	article
author	Guobo Wang Minglu Ma Lili Jiang Fengyun Chen Liansheng Xu
author_facet	Guobo Wang Minglu Ma Lili Jiang Fengyun Chen Liansheng Xu
author_sort	Guobo Wang
title	Multiple imputation of maritime search and rescue data at multiple missing patterns.
title_short	Multiple imputation of maritime search and rescue data at multiple missing patterns.
title_full	Multiple imputation of maritime search and rescue data at multiple missing patterns.
title_fullStr	Multiple imputation of maritime search and rescue data at multiple missing patterns.
title_full_unstemmed	Multiple imputation of maritime search and rescue data at multiple missing patterns.
title_sort	multiple imputation of maritime search and rescue data at multiple missing patterns.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/e3e52005007544e891a447859b89ec56
work_keys_str_mv	AT guobowang multipleimputationofmaritimesearchandrescuedataatmultiplemissingpatterns AT mingluma multipleimputationofmaritimesearchandrescuedataatmultiplemissingpatterns AT lilijiang multipleimputationofmaritimesearchandrescuedataatmultiplemissingpatterns AT fengyunchen multipleimputationofmaritimesearchandrescuedataatmultiplemissingpatterns AT lianshengxu multipleimputationofmaritimesearchandrescuedataatmultiplemissingpatterns
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Multiple imputation of maritime search and rescue data at multiple missing patterns.

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