Statistical Matching Techniques Using the Robust Regression Model

로버스트 회귀모형을 이용한 자료결합방법

Jhun, Myoung-Shic;Jung, Ji-Song;Park, Hye-Jin

  • Published : 2008.12.31


Statistical matching techniques whose aim is to achieve a complete data file from different sources. Since the statistical matching method proposed by Rubin (1986) assumes the multivariate normality for data, using this method to data which violates the assumption would involve some problems. This research proposed the statistical matching method using robust regression as an alternative to the linear regression. Furthermore, we carried out a simulation study to compare the performance of the robust regression model and the linear regression model for the statistical matching.


Statistical matching method;robust regression model;correlation;coefficient of determination


  1. Rassler, S. (2004). Data fusion: Identification problems, validity and multiple imputation, Austrian Journal of Statistics, 33, 153-171
  2. Rassler, S. (2002). Statistical Matching: A Frequentist Theory, Practical Applications, and Alternative Bayesian Approaches, Springer-Verlag, New York
  3. Moriarity, C. and Scheuren, F. (2001). Statistical matching: A paradigm for assessing the uncertainty in the procedure, Journal of official Statistics, 17, 407-422
  4. Kadane, J. B. (1978). Some statistical problems in merging data files, In 1978 Compendium of Tax Research, Washington, DC:U.S. Department of the Treasury, 159-171. (Reprinted in Journal of Offcial Statistics, 17, 423-433.)
  5. Rubin, D. B. (1986). Statistical matching using file concatenation with adjusted weights and multiple imputations, Journal of Business & Economic Statistics, 4, 87-94