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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0252129 |
| _version_ | 1818401547375083520 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-14T07:54:12Z |
| format | Article |
| id | doaj.art-e3e52005007544e891a447859b89ec56 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-14T07:54:12Z |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-e3e52005007544e891a447859b89ec562022-12-21T23:10:36ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01166e025212910.1371/journal.pone.0252129Multiple imputation of maritime search and rescue data at multiple missing patterns.Guobo WangMinglu MaLili JiangFengyun ChenLiansheng XuBased 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.https://doi.org/10.1371/journal.pone.0252129 |
| spellingShingle | Guobo Wang Minglu Ma Lili Jiang Fengyun Chen Liansheng Xu Multiple imputation of maritime search and rescue data at multiple missing patterns. PLoS ONE |
| title | 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_short | 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 |
| url | https://doi.org/10.1371/journal.pone.0252129 |
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