[gov3009-l] Applied Statistics 2/1/2017 --- Paul von Hippel

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.