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Invariant and Metric Free Proximities for Data Matching: An R Package

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  • Iacus, Stefano
  • Porro, Giuseppe

Abstract

Data matching is a typical statistical problem in non experimental and/or observational studies or, more generally, in cross-sectional studies in which one or more data sets are to be compared. Several methods are available in the literature, most of which based on a particular metric or on statistical models, either parametric or nonparametric. In this paper we present two methods to calculate a proximity which have the property of being invariant under monotonic transformations. These methods require at most the notion of ordering. An open-source software in the form of a R package is also presented.

Suggested Citation

  • Iacus, Stefano & Porro, Giuseppe, 2008. "Invariant and Metric Free Proximities for Data Matching: An R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i11).
    • Handle: RePEc:jss:jstsof:v:025:i11
    DOI: http://hdl.handle.net/10.18637/jss.v025.i11
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    References listed on IDEAS

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    1. Iacus, Stefano M. & Porro, Giuseppe, 2007. "Missing data imputation, matching and other applications of random recursive partitioning," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 773-789, October.
    2. Giuseppe Porro & Stefano Maria Iacus, 2009. "Random Recursive Partitioning: a matching method for the estimation of the average treatment effect," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(1), pages 163-185.
    3. Ho, Daniel E. & Imai, Kosuke & King, Gary & Stuart, Elizabeth A., 2007. "Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference," Political Analysis, Cambridge University Press, vol. 15(3), pages 199-236, July.
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    Cited by:

    1. Doove, L.L. & Van Buuren, S. & Dusseldorp, E., 2014. "Recursive partitioning for missing data imputation in the presence of interaction effects," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 92-104.
    2. Humera Razzak & Christian Heumann, 2019. "Hybrid Multiple Imputation In A Large Scale Complex Survey," Statistics in Transition New Series, Polish Statistical Association, vol. 20(4), pages 33-58, December.
    3. Razzak Humera & Heumann Christian, 2019. "Hybrid Multiple Imputation In A Large Scale Complex Survey," Statistics in Transition New Series, Statistics Poland, vol. 20(4), pages 33-58, December.

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