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An application of nonparametric regression to missing data in large market surveys

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  • Gary Madden
  • Nicholas Apergis
  • Paul Rappoport
  • Aniruddha Banerjee

Abstract

Non-response (or missing data) is often encountered in large-scale surveys. To enable the behavioural analysis of these data sets, statistical treatments are commonly applied to complete or remove these data. However, the correctness of such procedures critically depends on the nature of the underlying missingness generation process. Clearly, the efficacy of applying either case deletion or imputation procedures rests on the unknown missingness generation mechanism. The contribution of this paper is twofold. The study is the first to propose a simple sequential method to attempt to identify the form of missingness. Second, the effectiveness of the tests is assessed by generating (experimentally) nine missing data sets by imposed MCAR, MAR and NMAR processes, with data removed.

Suggested Citation

  • Gary Madden & Nicholas Apergis & Paul Rappoport & Aniruddha Banerjee, 2018. "An application of nonparametric regression to missing data in large market surveys," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(7), pages 1292-1302, May.
    • Handle: RePEc:taf:japsta:v:45:y:2018:i:7:p:1292-1302
    DOI: 10.1080/02664763.2017.1369498
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    Cited by:

    1. Chunhua Chen & Jianwei Ren & Lijun Tang & Haohua Liu, 2020. "Additive integer-valued data envelopment analysis with missing data: A multi-criteria evaluation approach," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-20, June.

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