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Robust correspondence analysis

Author

Listed:
  • Marco Riani
  • Anthony C. Atkinson
  • Francesca Torti
  • Aldo Corbellini

Abstract

Correspondence analysis is a method for the visual display of information from two‐way contingency tables. We introduce a robust form of correspondence analysis based on minimum covariance determinant estimation. This leads to the systematic deletion of outlying rows of the table and to plots of greatly increased informativeness. Our examples are trade flows of clothes and consumer evaluations of the perceived properties of cars. The robust method requires that a specified proportion of the data be used in fitting. To accommodate this requirement we provide an algorithm that uses a subset of complete rows and one row partially, both sets of rows being chosen robustly. We prove the convergence of this algorithm.

Suggested Citation

  • Marco Riani & Anthony C. Atkinson & Francesca Torti & Aldo Corbellini, 2022. "Robust correspondence analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1381-1401, November.
    • Handle: RePEc:bla:jorssc:v:71:y:2022:i:5:p:1381-1401
    DOI: 10.1111/rssc.12580
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    References listed on IDEAS

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    1. James M. Boyett, 1979. "Random R×C Tables with Given Row and Column Totals," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 28(3), pages 329-332, November.
    2. Andrea Cerioli & Marco Riani & Anthony C. Atkinson & Aldo Corbellini, 2018. "The power of monitoring: how to make the most of a contaminated multivariate sample," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(4), pages 559-587, December.
    3. W. M. Patefield, 1981. "An Efficient Method of Generating Random R × C Tables with Given Row and Column Totals," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 30(1), pages 91-97, March.
    4. Andrea Cerioli & Marco Riani & Anthony C. Atkinson & Aldo Corbellini, 2018. "Rejoinder to the discussion of “The power of monitoring: how to make the most of a contaminated multivariate sample”," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(4), pages 661-666, December.
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    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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