Rank‐based inference with responses missing not at random

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  • Author: Huybrechts F. Bindele and Akim Adekpedjou
  • Date: 10 May 2019
  • Copyright: Image copyright of Patrick Rhodes

Missing data have become almost inevitable whenever data are collected. In this paper, interest is given to responses missing not at random in the context of regression modeling. Many of the existing methods for estimating the model parameters lack robustness or efficiency. In a paper published in The Canadian Journal of Statistics, the authors propose a robust and efficient approach towards estimating the true regression parameters when some responses in the regression model are missing not at random.

The paper is available via the link here and the authors explain their findings in further detail below:

Rank‐based inference with responses missing not at random

Huybrechts F. Bindele and Akim Adekpedjou

The Canadian Journal of Statistics, Volume 46, Issue 3, September 2018, pages 501-528

thumbnail image: Rank‐based inference with responses missing not at random

Missing data has become almost inevitable whenever data are collected. In this paper, the interest is given to responses missing not at random in the context of regression modeling. Many of the existing methods for estimating the model parameters lack robustness and/or efficiency. A robust and efficient approach toward estimating the true regression parameters when some responses in the regression model are missing not at random is proposed. Large sample properties of the proposed estimator are established under mild regularity conditions. Monte Carlo simulation experiments are carried out and show that the proposed estimator is more efficient than the least squares estimator whenever the model error distribution is heavy tailed, contaminated and/or when data contain gross outliers. Finally, the method is illustrated using the ACTG protocol 315 data.

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