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Browsing by Subject "Multiple Imputation"
Now showing items 1-6 of 6
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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 ... -
Multiple Imputation Methods for Nonignorable Nonresponse, Adaptive Survey Design, and Dissemination of Synthetic Geographies
(2014)This thesis presents methods for multiple imputation that can be applied to missing data and data with confidential variables. Imputation is useful for missing data because it results in a data set that can be analyzed with ... -
Multiple Imputation on Missing Values in Time Series Data
(2015)Financial stock market data, for various reasons, frequently contain missing values. One reason for this is that, because the markets close for holidays, daily stock prices are not always observed. This creates ... -
Some Recent Advances in Non- and Semiparametric Bayesian Modeling with Copulas, Mixtures, and Latent Variables
(2013)This thesis develops flexible non- and semiparametric Bayesian models for mixed continuous, ordered and unordered categorical data. These methods have a range of possible applications; the applications considered in this ...
