DOI QR코드

DOI QR Code

Non-identifiability and testability of missing mechanisms in incomplete two-way contingency tables

  • Park, Yousung (Department of Statistics, Korea University) ;
  • Oh, Seung Mo (Department of Statistics, Korea University) ;
  • Kwon, Tae Yeon (Department of International Finance, Hankuk University of Foreign Studies)
  • Received : 2021.01.22
  • Accepted : 2021.03.26
  • Published : 2021.05.31

Abstract

We showed that any missing mechanism is reproduced by EMAR or MNAR with equal fit for observed likelihood if there are non-negative solutions of maximum likelihood equations. This is a generalization of Molenberghs et al. (2008) and Jeon et al. (2019). Nonetheless, as MCAR becomes a nested model of MNAR, a natural question is whether or not MNAR and MCAR are testable by using the well-known three statistics, LR (Likelihood ratio), Wald, and Score test statistics. Through simulation studies, we compared these three statistics. We investigated to what extent the boundary solution affect tesing MCAR against MNAR, which is the only testable pair of missing mechanisms based on observed likelihood. We showed that all three statistics are useful as long as the boundary proximity is far from 1.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2018R1C1B5043739). This work was supported by Hankuk University of Foreign Studies Research Fund.

References

  1. Baker SG, Rosenberger WF, and Dersimonian R (1992). Closed-form estimates for missing counts in two-way contingency tables, Statistics in Medicine, 11, 643-657. https://doi.org/10.1002/sim.4780110509
  2. Ibrahim JG, Zhu H, and Tang N (2008). Model selection criteria for missing-data problems using the em algorithm, Journal of the American Statistical Association, 103, 1648-1658. https://doi.org/10.1198/016214508000001057
  3. Jeon S, Kwon TY, and Park Y (2019). Variable-based missing mechanism for an incomplete contingency table with unit missingness, Statistics & Probability Letters, 146, 90-96. https://doi.org/10.1016/j.spl.2018.11.006
  4. Little RJ (1988). A test of missing completely at random for multivariate data with missing values, Journal of the American statistical Association, 83, 1198-1202. https://doi.org/10.1080/01621459.1988.10478722
  5. Little RJ (1994). A class of pattern-mixture models for normal incomplete data, Biometrika, 81, 471-483. https://doi.org/10.1093/biomet/81.3.471
  6. Little RJ (2008). Selection and pattern-mixture models, Longitudinal Data Analysis(pp.409-431), Chapman and Hall, London.
  7. Little RJ and Rubin DB (2019). Statistical Analysis with Missing Data, 793, Wiley Blackwell, England.
  8. Molenberghs G, Beunckens C, Sotto C, and Kenward MG (2008). Every missingness not at random model has a missingness at random counterpart with equal fit, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 70, 371-388. https://doi.org/10.1111/j.1467-9868.2007.00640.x
  9. Park Y, Kim D, and Kim S (2014). Identification of the occurrence of boundary solutions in a contingency table with nonignorable nonresponse, Statistics & Probability Letters, 93, 34-40. https://doi.org/10.1016/j.spl.2014.06.011
  10. Rubin DB, Stern HS, and Vehovar V (1995). Handling "don't know" survey responses: the case of the slovenian plebiscite, Journal of the American Statistical Association, 90, 822-828. https://doi.org/10.2307/2291315