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Comparison of imputation methods for handling missing categorical data with univariate pattern
This paper examines the sample proportions estimates in the presence of univariate missing categorical data. A database about smoking habits (2011 National Addiction Survey of Mexico) was used to create simulated yet realistic datasets at rates 5% and 15% of missingness, each for MCAR, MAR and MNAR...
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| Published in: | Revista de métodos cuantitativos para la economía y la empresa 2014, Vol.17, p.101-120 |
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| container_title | Revista de métodos cuantitativos para la economía y la empresa |
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| creator | Torres Munguia, Juan Armando |
| description | This paper examines the sample proportions estimates in the presence of univariate missing categorical data. A database about smoking habits (2011 National Addiction Survey of Mexico) was used to create simulated yet realistic datasets at rates 5% and 15% of missingness, each for MCAR, MAR and MNAR mechanisms. Then the performance of six methods for addressing missingness is evaluated: listwise, mode imputation, random imputation, hot-deck, imputation by polytomous regression and random forests. Results showed that the most effective methods for dealing with missing categorical data in most of the scenarios assessed in this paper were hot-deck and polytomous regression approaches. |
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| identifier | ISSN: 1886-516X |
| ispartof | Revista de métodos cuantitativos para la economía y la empresa, 2014, Vol.17, p.101-120 |
| issn | 1886-516X 1886-516X |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1803808330 |
| source | International Bibliography of the Social Sciences (IBSS); Publicly Available Content Database (Proquest) (PQ_SDU_P3); ABI/INFORM global |
| subjects | Addictions Data collection Forests Habits Hot-deck Imputation methods Missing categorical data Polytomous regression Random forests Regression analysis Smoking Smoking habits |
| title | Comparison of imputation methods for handling missing categorical data with univariate pattern |
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