Block-Conditional Missing at Random Models for Missing Data

Handling Missing Covariates in Conditional Mixture Models Under Missing at Random Assumptions.

Mixture modeling is a popular method that accounts for unobserved population heterogeneity using multiple latent classes that differ in response patterns. Psychologists use conditional mixture models to incorporate covariates into between-class and/or within-class regressions. Although psychologists often have missing covariate data, conditional mixtures are currently fit with a conditional lik...

Missing at Random in Graphical Models

The notion of missing at random (MAR) plays a central role in the theory underlying current methods for handling missing data. However the standard definition of MAR is difficult to interpret in practice. In this paper, we assume the missing data model is represented as a directed acyclic graph that not only encodes the dependencies among the variables but also explicitly portrays the causal me...

Efficient restricted estimators for conditional mean models with missing data

Consider a conditional mean model with missing data on the response or explanatory variables due to two-phase sampling or nonresponse. Robins et al. (1994) introduced a class of augmented inverse-probability-weighted estimators, depending on a vector of functions of explanatory variables and a vector of functions of coarsened data. Tsiatis (2006) studied two classes of restricted estimators, cl...

Distinguishing "Missing at Random"i and "Missing Completely at Randomn"

Efficiency transfer for regression models with responses missing at random

We consider independent observations on a random pair (X,Y ), where the response Y is allowed to be missing at random but the covariate vector X is always observed. We demonstrate that characteristics of the conditional distribution of Y given X can be estimated efficiently using complete case analysis, i.e., one can simply omit incomplete cases and work with an appropriate efficient estimator ...