Discrete Choice Models for Nonmonotone Nonignorable Missing Data: Identification and Inference

Discrete Choice Models for Nonmonotone Nonignorable Missing Data: Identification and Inference

Nonmonotone missing data arise routinely in empirical studies of social and health sciences, and when ignored, can induce selection bias and loss of efficiency. In practice, it is common to account for nonresponse under a missing-at-random assumption which although convenient, is rarely appropriate when nonresponse is nonmonotone. Likelihood and Bayesian missing data methodologies often require...

Likelihood-based Inference with Nonignorable Missing Responses and Covariates in Models for Discrete Longitudinal Data

We propose methods for estimating parameters in two types of models for discrete longitudinal data in the presence of nonignorable missing responses and covariates. We first present the generalized linear model with random effects, also known as the generalized linear mixed model. We specify a missing data mechanism and a missing covariate distribution and incorporate them into the complete dat...

Parametric fractional imputation for mixed models with nonignorable missing data

Inference in the presence of non-ignorable missing data is a widely encountered and difficult problem in statistics. Imputation is often used to facilitate parameter estimation, which allows one to use the complete sample estimators on the imputed data set. We develop a parametric fractional imputation (PFI) method proposed by Kim (2011), which simplifies the computation associated with the EM ...

Data Models for Discrete Choice

Graphical Models for Inference with Missing Data

We address the problem of recoverability i.e. deciding whether there exists a consistent estimator of a given relation Q, when data are missing not at random. We employ a formal representation called ‘Missingness Graphs’ to explicitly portray the causal mechanisms responsible for missingness and to encode dependencies between these mechanisms and the variables being measured. Using this represe...