2025-09-18 ワシントン大学セントルイス校
Web要約 の発言:
Lead often makes its way into humans through aging, corroded pipes.
<関連情報>
- https://source.washu.edu/2025/09/new-study-may-help-uncover-childhood-lead-exposures-true-impact/
- https://projecteuclid.org/journals/bayesian-analysis/advance-publication/Using-Auxiliary-Marginal-Quantiles-for-Gaussian-Copula-Models-with-Nonignorable/10.1214/25-BA1551.full
無視できない欠測データを有するガウス・コピュラモデルにおける補助的限界分位数の利用 Using Auxiliary Marginal Quantiles for Gaussian Copula Models with Nonignorable Missing Data
Joseph Feldman, Jerome P. Reiter, Daniel R. Kowal
Bayesian Analysis Advance Publication 1-29 (2025)
DOI:DOI: 10.1214/25-BA1551
Abstract
We present methods for parameter estimation and multiple imputation with Gaussian copula models in the presence of nonignorable missing data. Our approach uses Bayesian data integration to combine (i) a Gaussian copula model for all study variables and missingness indicators, which allows arbitrary marginal distributions, nonignorable missingness, and other dependencies, and (ii) auxiliary information in the form of marginal quantiles for some study variables. We prove that, remarkably, one only needs a small set of accurately-specified quantiles to estimate the copula correlation consistently. The remaining marginal distribution functions are inferred nonparametrically and jointly with the copula parameters using an efficient Markov Chain Monte Carlo (MCMC) algorithm. We also characterize the (additive) nonignorable missingness mechanism implied by the copula model. Simulations confirm the effectiveness of this approach for multivariate imputation with nonignorable missing data. We apply the model to analyze associations between lead exposure and end-of-grade test scores for 170,000 North Carolina students. Lead exposure has nonignorable missingness: children with higher exposure are more likely to be measured. We elicit marginal quantiles for lead exposure using statistics provided by the Centers for Disease Control and Prevention. Multiple imputation inferences under our model support stronger, more adverse associations between lead exposure and educational outcomes relative to complete case and missing-at-random analyses.**
