Efficient Multiple Imputation for Diverse Data in Python and R: MIDASpy and rMIDAS
This paper introduces software packages for efficiently imputing missing data using deep learning methods in Python (MIDASpy) and R (rMIDAS). The packages implement a recently developed approach to multiple imputation known as MIDAS, which involves introducing additional missing values into the dat...
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| Language: | English |
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Foundation for Open Access Statistics
2023-10-01
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| Series: | Journal of Statistical Software |
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| Online Access: | https://www.jstatsoft.org/index.php/jss/article/view/4379 |
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| author | Ranjit Lall Thomas Robinson |
| author_facet | Ranjit Lall Thomas Robinson |
| author_sort | Ranjit Lall |
| collection | DOAJ |
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This paper introduces software packages for efficiently imputing missing data using deep learning methods in Python (MIDASpy) and R (rMIDAS). The packages implement a recently developed approach to multiple imputation known as MIDAS, which involves introducing additional missing values into the dataset, attempting to reconstruct these values with a type of unsupervised neural network known as a denoising autoencoder, and using the resulting model to draw imputations of originally missing data. These steps are executed by a fast and flexible algorithm that expands both the quantity and the range of data that can be analyzed with multiple imputation. To help users optimize the algorithm for their particular application, MIDASpy and rMIDAS offer a host of user-friendly tools for calibrating and validating the imputation model. We provide a detailed guide to these functionalities and demonstrate their usage on a large real dataset.
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| format | Article |
| id | doaj-art-126f3c2b46284d60b714aedaea02dc19 |
| institution | DOAJ |
| issn | 1548-7660 |
| language | English |
| publishDate | 2023-10-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj-art-126f3c2b46284d60b714aedaea02dc192025-08-20T02:39:47ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602023-10-01107110.18637/jss.v107.i09Efficient Multiple Imputation for Diverse Data in Python and R: MIDASpy and rMIDASRanjit Lall0Thomas Robinson1University of OxfordLondon School of Economics and Political Science This paper introduces software packages for efficiently imputing missing data using deep learning methods in Python (MIDASpy) and R (rMIDAS). The packages implement a recently developed approach to multiple imputation known as MIDAS, which involves introducing additional missing values into the dataset, attempting to reconstruct these values with a type of unsupervised neural network known as a denoising autoencoder, and using the resulting model to draw imputations of originally missing data. These steps are executed by a fast and flexible algorithm that expands both the quantity and the range of data that can be analyzed with multiple imputation. To help users optimize the algorithm for their particular application, MIDASpy and rMIDAS offer a host of user-friendly tools for calibrating and validating the imputation model. We provide a detailed guide to these functionalities and demonstrate their usage on a large real dataset. https://www.jstatsoft.org/index.php/jss/article/view/4379missing datamultiple imputationmachine learningPythonR |
| spellingShingle | Ranjit Lall Thomas Robinson Efficient Multiple Imputation for Diverse Data in Python and R: MIDASpy and rMIDAS Journal of Statistical Software missing data multiple imputation machine learning Python R |
| title | Efficient Multiple Imputation for Diverse Data in Python and R: MIDASpy and rMIDAS |
| title_full | Efficient Multiple Imputation for Diverse Data in Python and R: MIDASpy and rMIDAS |
| title_fullStr | Efficient Multiple Imputation for Diverse Data in Python and R: MIDASpy and rMIDAS |
| title_full_unstemmed | Efficient Multiple Imputation for Diverse Data in Python and R: MIDASpy and rMIDAS |
| title_short | Efficient Multiple Imputation for Diverse Data in Python and R: MIDASpy and rMIDAS |
| title_sort | efficient multiple imputation for diverse data in python and r midaspy and rmidas |
| topic | missing data multiple imputation machine learning Python R |
| url | https://www.jstatsoft.org/index.php/jss/article/view/4379 |
| work_keys_str_mv | AT ranjitlall efficientmultipleimputationfordiversedatainpythonandrmidaspyandrmidas AT thomasrobinson efficientmultipleimputationfordiversedatainpythonandrmidaspyandrmidas |