Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/113873
Authors: 
Torres Munguía, Juan Armando
Year of Publication: 
2014
Citation: 
[Journal:] Revista de Métodos Cuantitativos para la Economía y la Empresa [ISSN:] 1886-516X [Volume:] 17 [Year:] 2014 [Pages:] 101-120
Abstract: 
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.
Subjects: 
imputation methods
hot-deck
polytomous regression
random forests
smoking habits
missing categorical data
JEL: 
C18
C80
C83
Document Type: 
Article

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