Improving estimation efficiency for regression with MNAR covariates

Corrigendum: Improving upon the efficiency of complete case analysis when covariates are MNAR (10.1093/biostatistics/kxu023).

2. APPLICATION TO NHANES: CORRECTION OF VARIABLE DEFINITION The main manuscript stated that loge(average no. drinks per day + 1) was used as covariate in the model of interest in the analysis of data from NHANES. This was incorrect: loge(average no. drinks per day) was used as covariate. For the ACC and MI estimators, the number of drinks minus one was imputed using negative binomial regression...

Improving upon the efficiency of complete case analysis when covariates are MNAR

Missing values in covariates of regression models are a pervasive problem in empirical research. Popular approaches for analyzing partially observed datasets include complete case analysis (CCA), multiple imputation (MI), and inverse probability weighting (IPW). In the case of missing covariate values, these methods (as typically implemented) are valid under different missingness assumptions. I...

Estimation in zero-inflated binomial regression with missing covariates

Semiparametric Maximum Likelihood Estimation in Parametric Regression with Missing Covariates

Regression Discontinuity Design with Covariates

Regression Discontinuity Design with Covariates In this paper, the regression discontinuity design (RDD) is generalized to account for differences in observed covariates X in a fully nonparametric way. It is shown that the treatment effect can be estimated at the rate for one-dimensional nonparametric regression irrespective of the dimension of X. It thus extends the analysis of Hahn, Todd, and...