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Multivariate Data Imputation using Trees

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  • Bárcena Ruiz, María Jesús
  • Tusell Palmer, Fernando Jorge

Abstract

We address the problem of completing two files with records containing a fully observed common subset of variables. The tecnique investigated involves the use of regression and/or classification trees. An extension of current methodology (the intersection-seeking or "forest-climbing" algorithm) is proposed to deal with multivariate response variables. The method is demonstrated and shown to be feasible and have some desirable properties.

Suggested Citation

  • Bárcena Ruiz, María Jesús & Tusell Palmer, Fernando Jorge, 2002. "Multivariate Data Imputation using Trees," BILTOKI 1134-8984, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
    • Handle: RePEc:ehu:biltok:5728
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    File URL: https://addi.ehu.es/handle/10810/5728
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    References listed on IDEAS

    as
    1. Ciampi, Antonio, 1991. "Generalized regression trees," Computational Statistics & Data Analysis, Elsevier, vol. 12(1), pages 57-78, August.
    2. Rubin, Donald B, 1986. "Statistical Matching Using File Concatenation with Adjusted Weights and Multiple Imputations," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 87-94, January.
    3. Eric Schulte Nordholt, 1998. "Imputation: Methods, Simulation Experiments and Practical Examples," International Statistical Review, International Statistical Institute, vol. 66(2), pages 157-180, August.
    4. Donald Rubin, 1991. "EM and beyond," Psychometrika, Springer;The Psychometric Society, vol. 56(2), pages 241-254, June.
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