Treatment effect identification using two-level designs with partially ignorable missing data
From MaRDI portal
Publication:6122312
DOI10.1016/j.ins.2022.08.024MaRDI QIDQ6122312
Publication date: 27 March 2024
Published in: Information Sciences (Search for Journal in Brave)
Cites Work
- Unnamed Item
- Introduction to double robust methods for incomplete data
- Bayesian approaches for missing not at random outcome data: the role of identifying restrictions
- EM algorithms for multivariate Gaussian mixture models with truncated and censored data
- Partial identification with missing data: concepts and findings
- Algorithmic Aspects of Machine Learning
- Partial and latent ignorability in missing-data problems
- A general instrumental variable framework for regression analysis with outcome missing not at random
- On Inverse Probability Weighting for Nonmonotone Missing at Random Data
- Pattern-Mixture Models for Multivariate Incomplete Data
- Theory and application of uniform designs
- The Automatic Construction of Bootstrap Confidence Intervals
- Bayesian models for data missing not at random in health examination surveys
- Identification and Inference in Nonlinear Difference-in-Differences Models
- Design of order-of-addition experiments
- EPEM: efficient parameter estimation for multiple class monotone missing data
This page was built for publication: Treatment effect identification using two-level designs with partially ignorable missing data