Abstract
We consider two-level models where a continuous response R and continuous covariates C are assumed missing at random. Inferences based on maximum likelihood or Bayes are routinely made by estimating their joint normal distribution from observed data and
. However, if the model for R given C includes random coefficients, interactions, or polynomial terms, their joint distribution will be nonstandard. We propose a family of unique factorizations involving selected “provisionally known random effects” u such that
is normally distributed and u is a low-dimensional normal random vector; we approximate
via adaptive Gauss-Hermite quadrature. For polynomial models, the approximation is exact but, in any case, can be made as accurate as required given sufficient computation time. The model incorporates random effects as explanatory variables, reducing bias due to measurement error. By construction, our factorizations solve problems of compatibility among fully conditional distributions that have arisen in Bayesian imputation based on the Gibbs Sampler. We spell out general rules for selecting u, and show that our factorizations can support fully compatible Bayesian methods of imputation using the Gibbs Sampler. Supplementary materials for this article are available online.
Supplementary Materials
The supplementary files that contain R and C executable codes, data sets and initial values to reproduce and may be downloaded. See README_supplementarymaterials.txt for instruction.
Acknowledgments
We thank two anonymous reviewers and an associate editor for their helpful comments, Craig Enders and Brian Keller for providing Blimp simulation codes, and Dongho Shin for helping Blimp simulation in R environment.
Disclosure Statement
The authors report there are no competing interests to declare.