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On Proxy Variables and Categorical Data Fusion

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  • Zhang Li-Chun

    (University of Southampton, S3RI/Social Statistics and Demography, Highfield Southampton SO17 1BJ, UK and Statistics Norway, P.O. Box 8131 Dep. 0033 Oslo, Norway.)

Abstract

The problem of inference about the joint distribution of two categorical variables based on knowledge or observations of their marginal distributions, to be referred to as categorical data fusion in this paper, is relevant in statistical matching, ecological inference, market research, and several other related fields. This article organizes the use of proxy variables, to be distinguished from other auxiliary variables, both in terms of their effects on the uncertainty of fusion and the techniques of fusion. A measure of the gains of efficiency is provided, which incorporates both the identification uncertainty associated with data fusion and the sampling uncertainty that arises when the theoretical bounds of the uncertainty space are unknown and need to be estimated. Several existing techniques for generating fusion distributions (or datasets) are described and some new ones proposed. Analysis of real-life data demonstrates empirically that proxy variables can make data fusion more precise and the constructed fusion distribution more plausible.

Suggested Citation

  • Zhang Li-Chun, 2015. "On Proxy Variables and Categorical Data Fusion," Journal of Official Statistics, Sciendo, vol. 31(4), pages 783-807, December.
    • Handle: RePEc:vrs:offsta:v:31:y:2015:i:4:p:783-807:n:13
    DOI: 10.1515/jos-2015-0045
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    References listed on IDEAS

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    1. Conti, Pier Luigi & Marella, Daniela & Scanu, Mauro, 2008. "Evaluation of matching noise for imputation techniques based on nonparametric local linear regression estimators," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 354-365, December.
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