The Effect of Sample Size and Missingness on Inference with Missing Data

When are inferences (whether Direct-Likelihood, Bayesian, or Frequentist) obtained from partial data valid? This paper answers this question by offering a new theory about inference with missing data. It proves that as the sample size increases and the extent of missingness decreases, the mean-loglikelihood function generated by partial data and that ignores the missingness mechanism will almost surely converge uniformly to that which would have been generated by complete data; and if the data are Missing at Random, this convergence depends only on sample size. Thus, inferences on partial data, such as posterior modes, uncertainty estimates, confidence intervals, likelihood ratios, and indeed, all quantities or features derived from the partial-data loglikelihood function, will approximate their true values (what they would have been given complete data). This adds to previous research which has only proved the consistency of the posterior mode. Practical implications of this result are discussed, and the theory is tested on a previous study of International Human Rights Law.