ALERT: This system is being upgraded on Tuesday December 12. It will not be available
for use for several hours that day while the upgrade is in progress. Deposits to DukeSpace
will be disabled on Monday December 11, so no new items are to be added to the repository
while the upgrade is in progress. Everything should be back to normal by the end of
day, December 12.
Browsing by Subject "Missing Data"
Now showing items 1-4 of 4
-
A Comparison Of Multiple Imputation Methods For Categorical Data
(2015)This thesis evaluates the performance of several multiple imputation methods for categorical data, including multiple imputation by chained equations using generalized linear models, multiple imputation by chained equations ... -
Bayesian Models for Combining Information from Multiple Sources
(2022)This dissertation develops Bayesian methods for combining information from multiple sources. I focus on developing Bayesian bipartite modeling for simultaneous regression and record linkage, as well as leveraging auxiliary ... -
Bayesian Models for Imputing Missing Data and Editing Erroneous Responses in Surveys
(2019)This thesis develops Bayesian methods for handling unit nonresponse, item nonresponse, and erroneous responses in large scale surveys and censuses containing categorical data. I focus on applications to nested household ... -
Stochastic Latent Domain Approaches to the Recovery and Prediction of High Dimensional Missing Data
(2023)This work presents novel techniques for approaching missing data using generative models. The main focus of these techniques is on leveraging the latent spaces of generative models, both to improve inference performance ...
