Supplementary Material for “On the Use of Cauchy Prior Distributions for Bayesian Logistic
Regression”. In the supplementary material, we present additional simulation results for …

The paper extends existing models for multilevel multivariate data with mixed response
types to handle quite general types and patterns of missing data values in a wide range of …

We introduce a framework for estimating causal effects of binary and continuous treatments
in high dimensions. We show how posterior distributions of treatment and outcome models …

This paper considers the problem of imputation model uncertainty in the context of missing
data problems. We argue that so-called “Bayesianly proper” approaches to multiple …

In comparative effectiveness research, we are often interested in the estimation of an
average causal effect from large observational data (the main study). Often this data does …

Multiple imputation is a commonly used approach to deal with missing values. In this
approach, an imputer repeatedly imputes the missing values by taking draws from the …

In many moderate dimensional applications we have multiple response variables that are
associated with a common set of predictors. When the main objective is prediction of the …

Early identification of contaminated food products is crucial in reducing health burdens of
food-borne disease outbreaks. Analytic case-control studies are primarily used in this …

Predicting the annual frequency of tropical storms is of interest because it can provide basic
information towards improved preparation against these storms. Sea surface temperatures …

Variable selection is crucial for sparse modeling in this age of big data. Missing values are
common in data, and make variable selection more complicated. The approach of multiple …