A bias‐corrected estimator in multiple imputation for missing data

DOI Web Site Web Site PubMed 被引用文献3件 参考文献44件
  • Hiroaki Tomita
    Department of Statistical Science, School of Multidisciplinary Sciences SOKENDAI (The Graduate University for Advanced Studies) Tokyo Japan
  • Hironori Fujisawa
    Department of Statistical Science, School of Multidisciplinary Sciences SOKENDAI (The Graduate University for Advanced Studies) Tokyo Japan
  • Masayuki Henmi
    Department of Statistical Science, School of Multidisciplinary Sciences SOKENDAI (The Graduate University for Advanced Studies) Tokyo Japan

書誌事項

公開日
2018-05-29
資源種別
journal article
権利情報
  • http://onlinelibrary.wiley.com/termsAndConditions#vor
DOI
  • 10.1002/sim.7833
公開者
Wiley

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説明

<jats:p>Multiple imputation (MI) is one of the most popular methods to deal with missing data, and its use has been rapidly increasing in medical studies. Although MI is rather appealing in practice since it is possible to use ordinary statistical methods for a complete data set once the missing values are fully imputed, the method of imputation is still problematic. If the missing values are imputed from some parametric model, the validity of imputation is not necessarily ensured, and the final estimate for a parameter of interest can be biased unless the parametric model is correctly specified. Nonparametric methods have been also proposed for MI, but it is not so straightforward as to produce imputation values from nonparametrically estimated distributions. In this paper, we propose a new method for MI to obtain a consistent (or asymptotically unbiased) final estimate even if the imputation model is misspecified. The key idea is to use an imputation model from which the imputation values are easily produced and to make a proper correction in the likelihood function after the imputation by using the density ratio between the imputation model and the true conditional density function for the missing variable as a weight. Although the conditional density must be nonparametrically estimated, it is not used for the imputation. The performance of our method is evaluated by both theory and simulation studies. A real data analysis is also conducted to illustrate our method by using the Duke Cardiac Catheterization Coronary Artery Disease Diagnostic Dataset.</jats:p>

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