Abstract
Often, multivariate categorical data are incomplete in the sense that their overall joint probabilities are not available in the data base. Instead joint probabilities (or frequencies) of subsets of the variables are given. We provide a simple approach for “reconstructing” these overall joint probabilities from the information available in the subsets. We develop procedures for identifying “reconstruction ranges (or families),” selecting the best reconstruction family, and computing unbiased estimated probabilities. This approach is illustrated using a store choice data base.
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Hosseini, J.C. (2015). Reconstruction of Incomplete Multivariate Categorical Data: A Useful Tool for Marketing Research. In: King, R. (eds) Proceedings of the 1991 Academy of Marketing Science (AMS) Annual Conference. Developments in Marketing Science: Proceedings of the Academy of Marketing Science. Springer, Cham. https://doi.org/10.1007/978-3-319-17049-7_74
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DOI: https://doi.org/10.1007/978-3-319-17049-7_74
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