TY  - JOUR
T1  - Multiple imputation of missing categorical and continuous outcomes via Bayesian mixture models with local dependence
JF  - Journal of the American Statistical Association
Y1  - 2017
A1  - J. S. Murray
A1  - J. P. Reiter
KW  - Hierarchical mixture model
KW  - Missing data
KW  - Nonparametric Bayes
KW  - Stick-breaking process
AB  - We present a nonparametric Bayesian joint model for multivariate continuous and categorical variables, with the intention of developing a flexible engine for multiple imputation of missing values. The model fuses Dirichlet process mixtures of multinomial distributions for categorical variables with Dirichlet process mixtures of multivariate normal distributions for continuous variables. We incorporate dependence between the continuous and categorical variables by (i) modeling the means of the normal distributions as component-specific functions of the categorical variables and (ii) forming distinct mixture components for the categorical and continuous data with probabilities that are linked via a hierarchical model. This structure allows the model to capture complex dependencies between the categorical and continuous data with minimal tuning by the analyst. We apply the model to impute missing values due to item nonresponse in an evaluation of the redesign of the Survey of Income and Program Participation (SIPP). The goal is to compare estimates from a field test with the new design to estimates from selected individuals from a panel collected under the old design. We show that accounting for the missing data changes some conclusions about the comparability of the distributions in the two datasets. We also perform an extensive repeated sampling simulation using similar data from complete cases in an existing SIPP panel, comparing our proposed model to a default application of multiple imputation by chained equations. Imputations based on the proposed model tend to have better repeated sampling properties than the default application of chained equations in this realistic setting.
VL  - 111
IS  - 516
ER  - 

TY  - JOUR
T1  - A Bayesian Partial Identification Approach to Inferring the Prevalence of Accounting Misconduct
JF  - Journal of the American Statistical Association
Y1  - 2016
A1  - P. R. Hahn
A1  - J. S. Murray
A1  - I. Manolopoulou
AB  - This article describes the use of flexible Bayesian regression models for estimating a partially identified probability function. Our approach permits efficient sensitivity analysis concerning the posterior impact of priors on the partially identified component of the regression model. The new methodology is illustrated on an important problem where only partially observed data are available—inferring the prevalence of accounting misconduct among publicly traded U.S. businesses. Supplementary materials for this article are available online.
VL  - 111
UR  - http://www.tandfonline.com/doi/full/10.1080/01621459.2015.1084307
IS  - 513
ER  - 

TY  - RPRT
T1  - Flexible prior specification for partially identified nonlinear regression with binary responses
Y1  - 2014
A1  - P. R. Hahn
A1  - J. S. Murray
A1  - I. Manolopoulou
AB  - This paper adapts tree-based Bayesian regression models for estimating a partially identified probability function. In doing so, ideas from the recent literature on Bayesian partial identification are applied within a sophisticated applied regression context. Our approach permits efficient sensitivity analysis concerning the posterior impact of priors over the partially identified component of the regression model. The new methodology is illustrated on an important problem where we only have partially observed data -- inferring the prevalence of accounting misconduct among publicly traded U.S. businesses.
PB  - arXiv
UR  - https://arxiv.org/abs/1407.8430v1
IS  - 1407.8430
ER  -