Bias-corrected weighted least squares model averaging in linear measurement error models with randomly right-censored data

Repairing Locally Misspecified GMM: An Empirical Bayes Approach

Econometric models offer parsimonious but inexact approximations to data-generating processes. This paper studies the generalized method of moments (GMM) when exchangeable specification errors of order $n^{-1/2}$ contaminate the moment conditions. I develop estimators for the mean and variance of these specification er...

Rank-based Model Average Estimator for Average Treatment Effects

Model averaging emerges as an attractive ensemble technique that strategically synthesizes all available information from potential candidate models to enhance estimation accuracy. Despite of having been widely practiced in various areas in biostatistics and economics, its application to causal inference is rather limi...

A Data-Driven Shrinkage for Efficient Estimation in the Generalized Poisson Regression Model: Simulation and Application

Count data in practice rarely conform to the equidispersion assumption that the standard Poisson regression model imposes. When counts are overdispersed or underdispersed, the generalized Poisson regression model (GPRM) steps in as a more accommodating alternative, extending the classical framework to handle such varia...

Sandwich Variance Estimation in Scale Mixture of Skew‐Normal Linear Mixed Models

Linear mixed models are often used to analyze studies with repeated measurements over time, where the data present a correlation between observations of the same individual. However, one of the main challenges in using these models is correctly defining the covariance structure associated with error and random effects....

Linearization Variance Estimators for Survey Data: Some Recent Work

Imputation is all you need: double robustness, semiparametric efficiency, and automatic covariate balance for estimating the average treatment effect

Imputation-based causal estimation is typically viewed as relying exclusively on an outcome model, in contrast to augmented inverse-probability weighting, whose consistency is protected by fitting two nuisance models. This paper argues that this view can be misleading by highlighting a hidden dual weighting structure i...