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SHORT COMMUNICATIONS

Variable screening with missing covariates: a discussion of ‘statistical inference for nonignorable missing data problems: a selective review’ by Niansheng Tang and Yuanyuan Ju

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Pages 134-136 | Received 20 Aug 2018, Accepted 09 Sep 2018, Published online: 22 Sep 2018

References

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  • Huang, D. Y., Li, R. Z., & Wang, H. S. (2014). Feature screening for ultrahigh dimensional categorical data with applications. Journal of Business & Economic Statistics, 32, 237–244. doi: 10.1080/07350015.2013.863158
  • Lai, P., Liu, Y. M., Liu, Z., & Wan, Y. (2017). Model free feature screening for ultrahigh dimensional data with responses missing at random. Computational Statistics & Data Analysis, 105, 201–216. doi: 10.1016/j.csda.2016.08.008
  • Ni, L., Fang, F., & Wan, F. J. (2017). Adjusted pearson chi-square feature screening for multi-classification with ultrahigh dimensional data. Metrika, 80, 805–828. doi: 10.1007/s00184-017-0629-9
  • Wang, Q. H., & Li, Y. J. (2018). How to make model-free feature screening approaches for full data applicable to the case of missing response?. Scandinavian Journal of Statistics, 45, 324–346. doi: 10.1111/sjos.12290

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