A new imputation method for incomplete binary data

In data analysis problems where the data are represented by vectors of real numbers, it is often the case that some of the data-points will have “missing values”, meaning that one or more of the entries of the vector that describes the data-point is not observed. In this paper, we propose a new appr...

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Published in:Discrete Applied Mathematics 2011-06, Vol.159 (10), p.1040-1047
Main Authors: Subasi, Munevver Mine, Subasi, Ersoy, Anthony, Martin, Hammer, Peter L.
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Language:English
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Boolean similarity measure
Imputation
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description In data analysis problems where the data are represented by vectors of real numbers, it is often the case that some of the data-points will have “missing values”, meaning that one or more of the entries of the vector that describes the data-point is not observed. In this paper, we propose a new approach to the imputation of missing binary values. The technique we introduce employs a “similarity measure” introduced by Anthony and Hammer (2006)  [1]. We compare experimentally the performance of our technique with ones based on the usual Hamming distance measure and multiple imputation.
doi_str_mv 10.1016/j.dam.2011.01.024
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subjects Boolean similarity measure
Imputation
title A new imputation method for incomplete binary data
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