Publication: A Methodology for Quantifying the Effect of Missing Data on Decision Quality in Classification Problems
A Methodology for Quantifying the Effect of Missing Data on Decision Quality in Classification Problems
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Feldman, M., Even, A., & Parmet, Y. (2018). A Methodology for Quantifying the Effect of Missing Data on Decision Quality in Classification Problems. Communications in Statistics : Theory and Methods, 47(11), 2643–2663. https://doi.org/10.1080/03610926.2016.1277752
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Decision-making is often supported by decision models. This study suggests that the negative impact of poor data quality (DQ) on decision making is often mediated by biased model estimation. To highlight this perspective, we develop an analytical framework that links three quality levels – data, model, and decision. The general framework is first developed at a high-level, and then extended further toward understanding the effect of incomplete datasets on Linear Discriminant Analysis (LDA) classifiers. The interplay between the three
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- Feldman, Michael
- Even, Adir
- Parmet, Yisrael
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Feldman, M., Even, A., & Parmet, Y. (2018). A Methodology for Quantifying the Effect of Missing Data on Decision Quality in Classification Problems. Communications in Statistics : Theory and Methods, 47(11), 2643–2663. https://doi.org/10.1080/03610926.2016.1277752
