Rui Duan, C. Jason Liang, Pamela Shaw, Cheng Yong Tang, Yong Chen
arXiv 25 Mar 2020 · Statistics — Methodology · 1 citations (OpenAlex)
arXiv:2003.11181 · PDF · DOI · OpenAlex · Extracted main text
Practical problems with missing data are common, and statistical methods have been developed concerning the validity and/or efficiency of statistical procedures. On a central focus, there have been longstanding interests on the mechanism governing data missingness, and correctly deciding the appropriate mechanism is crucially relevant for conducting proper practical investigations. The conventional notions include the three common potential classes -- missing completely at random, missing at random, and missing not at random. In this paper, we present a new hypothesis testing approach for deciding between missing at random and missing not at random. Since the potential alternatives of missing at random are broad, we focus our investigation on a general class of models with instrumental variables for data missing not at random. Our setting is broadly applicable, thanks to that the model concerning the missing data is nonparametric, requiring no explicit model specification for the data missingness. The foundational idea is to develop appropriate discrepancy measures between estimators whose properties significantly differ only when missing at random does not hold. We show that our new hypothesis testing approach achieves an objective data oriented choice between missing at random or not. We demonstrate the feasibility, validity, and efficacy of the new test by theoretical analysis, simulation studies, and a real data analysis.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Zhao, J. and J. Shao (2015) Semiparametric pseudo-likelihoods in generalized linear models with non-ignorable missing data | 1.000 | 8 | 3 | 100% |
| 2 | Little, R. J. A. and D. B. Rubin (2019) Statistical Analysis with Missing Data\/ (3rd ed.) | 0.737 | 3 | 2 | 100% |
| 3 | Molenberghs, G. and M. G. Kenward (2007) Missing Data in Clinical Studies | 0.737 | 3 | 2 | 100% |
| 4 | Kim, J. K. and J. Shao (2013) Statistical methods for handling incomplete data | 0.644 | 2 | 2 | 100% |
| 5 | Tsiatis, A. A (2006) Semiparametric Theory and Missing Data | 0.644 | 2 | 2 | 100% |
| 6 | Wang, L., J. Shao, and F. Fang (2019) Propensity model selection with nonignorable nonresponse and instrument variable | 0.644 | 2 | 2 | 100% |
| 7 | White, I. R. and J. B. Carlin (2010, sep) (2010) Bias and efficiency of multiple imputation compared with complete-case analysis for missing covariate values | 0.644 | 2 | 2 | 100% |
| 8 | Hausman, J. A. (1978, nov) (1978) Specification tests in econometrics | 0.644 | 2 | 2 | 100% |
| 9 | Kim, J. K. and C. L. Yu (2011) A semi-parametric estimation of mean functionals with non-ignorable missing data | 0.511 | 2 | 1 | 100% |
| 10 | Molenberghs, G., G. Fitzmaurice, M. G. Kenward, A. Tsiatis, and G. V… (2014) Handbook of Missing Data Methodology | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 46 scored citations.