Examining Missing Data Mechanisms via Homogeneity of Parameters, Homogeneity of Distributions, and Multivariate Normality

• Activity Number: 650
• Type: Contributed
• Date/Time: Thursday, August 7, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistical Computing
• Author(s): Mortaza Jamshidian*+ and Ke Hai Yuan
• Companies: California State University, Fullerton and University of Notre Dame
• Keywords: incomplete data ; missing data mechanism ; sensitivity analysis ; Missing Completely at random ; nonparametric test ; multivariate normality

Abstract

We review methods of identifying missing data mechanisms MCAR, MAR, and MNAR. A number of tests deem rejection of homogeneity of mean and/or covariances (HMC) amongst observed data patterns as a means to reject MCAR. Utility of these tests as well as their shortcomings are discussed. More generally, tests of homogeneity of parameter estimates between various subsets of data are reviewed and their utility as tests of MCAR and MAR is pointed out. Since many tests of MCAR assume multinormality, methods to assess this assumption in the context of incomplete data are reviewed. Tests of homogeneity of distributions among observed data patterns for MCAR are also considered. A new nonparametric test of this type is proposed based on pairwise comparison of marginal distributions. Finally, methods of examining missing data mechanism based on sensitivity analysis including methods that model missing data mechanism based on logistic, probit, and latent variable regression models, as well as methods that do not require modeling of missing data mechanism are reviewed. The talk will conclude with some practical comments about the validity and utility of tests of missing data mechanism.