Missing at random, likelihood ignorability and model completeness

Missing at Random , Likelihood Ignorability and Model Completeness

This paper provides further insight into the key concept of missing at random (MAR) in incomplete data analysis. Following the usual selection modelling approach we envisage two models with separable parameters: a model for the response of interest and a model for the missing data mechanism (MDM). If the response model is given by a complete density family, then frequentist inference from the l...

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

What is the difference between missing completely at random and missing at random?

The terminology describing missingness mechanisms is confusing. In particular the meaning of 'missing at random' is often misunderstood, leading researchers faced with missing data problems away from multiple imputation, a method with considerable advantages. The purpose of this article is to clarify how 'missing at random' differs from 'missing completely at random' via an imagined dialogue be...

Partially linear single-index model with missing responses at random

This paper considers semiparametric partially linear single-index model with missing responses at random. Imputation approach is developed to estimate the regression coefficients, single-index coefficients and the nonparametric function, respectively. The imputation estimators for the regression coefficients and single-index coefficients are obtained by a stepwise approach. These estimators are...

Bayesian Single Index Model with Covariates Missing at Random

Bayesian single index model is a highly promising dimension reduction tool for an interpretable modeling of the non linear relationship between the response and its predictors. However, existing Bayesian tools in this area suffer from slow mixing of the Markov Chain Monte Carlo (MCMC) computational tool and also lack the ability to deal with missing covariates. To circumvent these practical pro...