Hi all,
This week at the Applied Statistics workshop we will be welcoming Paul von Hippel,
Associate Professor of Public Affairs at the University of Texas-Austin School of Public
Affairs. He will be presenting work entitled "Maximum likelihood multiple
imputation: A more efficient approach to repairing and analyzing incomplete data."
Please find the abstract below and on the website. The paper can be found here:
https://arxiv.org/abs/1210.0870
We will meet in CGIS Knafel Room 354 at noon and lunch will be provided.
Best,
Pam
Title: Maximum likelihood multiple imputation: A more efficient approach to repairing and
analyzing incomplete data
Abstract: Maximum likelihood multiple imputation (MLMI) is a form of multiple imputation
(MI) that imputes values conditionally on a maximum likelihood estimate of the parameters.
MLMI contrasts with the most popular form of MI, posterior draw multiple imputation
(PDMI), which imputes values conditionally on an estimate drawn at random from the
posterior distribution of the parameters. Despite being less popular, MLMI is less
computationally intensive and yields more efficient point estimates than PDMI. A barrier
to the use of MLMI has been the difficulty of estimating standard errors and confidence
intervals. We present three straightforward solutions to the standard error problem.