TY  - JOUR
T1  - Itemwise conditionally independent nonresponse modeling for incomplete multivariate data
JF  - Biometrika
Y1  - 2017
A1  - M. Sadinle
A1  - J.P. Reiter
KW  - Loglinear model
KW  - Missing not at random
KW  - Missingness mechanism
KW  - Nonignorable
KW  - Nonparametric saturated
KW  - Sensitivity analysis
AB  - We introduce a nonresponse mechanism for multivariate missing data in which each study variable and its nonresponse indicator are conditionally independent given the remaining variables and their nonresponse indicators. This is a nonignorable missingness mechanism, in that nonresponse for any item can depend on values of other items that are themselves missing. We show that, under this itemwise conditionally independent nonresponse assumption, one can define and identify nonparametric saturated classes of joint multivariate models for the study variables and their missingness indicators. We also show how to perform sensitivity analysis to violations of the conditional independence assumptions encoded by this missingness mechanism. Throughout, we illustrate the use of this modeling approach with data analyses.
VL  - 104
UR  - https://doi.org/10.1093/biomet/asw063
IS  - 1
ER  - 

TY  - JOUR
T1  - A nonparametric, multiple imputation-based method for the retrospective integration of data sets
JF  - Multivariate Behavioral Research
Y1  - 2015
A1  - M.M. Carrig
A1  - D. Manrique-Vallier
A1  - K. Ranby
A1  - J.P. Reiter
A1  - R. Hoyle
VL  - 50
UR  - http://www.tandfonline.com/doi/full/10.1080/00273171.2015.1022641
IS  - 4
ER  - 

TY  - JOUR
T1  - Semi-parametric selection models for potentially non-ignorable attrition in panel studies with refreshment samples
JF  - Political Analysis
Y1  - 2015
A1  - Y. Si
A1  - J.P. Reiter
A1  - D.S. Hillygus
VL  - 23
UR  - http://pan.oxfordjournals.org/cgi/reprint/mpu009?%20ijkey=joX8eSl6gyIlQKP&keytype=ref
ER  - 

TY  - JOUR
T1  - Stop or continue data collection: A nonignorable missing data approach for continuous variables
JF  - ArXiv
Y1  - 2015
A1  - T. Paiva
A1  - J.P. Reiter
KW  - Methodology
AB  - We present an approach to inform decisions about nonresponse followup sampling. The basic idea is (i) to create completed samples by imputing nonrespondents' data under various assumptions about the nonresponse mechanisms, (ii) take hypothetical samples of varying sizes from the completed samples, and (iii) compute and compare measures of accuracy and cost for different proposed sample sizes. As part of the methodology, we present a new approach for generating imputations for multivariate continuous data with nonignorable unit nonresponse. We fit mixtures of multivariate normal distributions to the respondents' data, and adjust the probabilities of the mixture components to generate nonrespondents' distributions with desired features. We illustrate the approaches using data from the 2007 U. S. Census of Manufactures.
UR  - http://arxiv.org/abs/1511.02189
IS  - 1511.02189
ER  - 

TY  - JOUR
T1  - Bayesian multiple imputation for large-scale categorical data with structural zeros
JF  - Survey Methodology
Y1  - 2014
A1  - D. Manrique-Vallier
A1  - J.P. Reiter
VL  - 40
UR  - http://www.stat.duke.edu/ jerry/Papers/SurvMeth14.pdf
ER  - 

TY  - CHAP
T1  - Disclosure risk evaluation for fully synthetic data
T2  - Privacy in Statistical Databases
Y1  - 2014
A1  - J. Hu
A1  - J.P. Reiter
A1  - Q. Wang
JF  - Privacy in Statistical Databases
PB  - Springer
CY  - Heidelberg
VL  - 8744
ER  - 

TY  - JOUR
T1  - Imputation of confidential data sets with spatial locations using disease mapping models
JF  - Statistics in Medicine
Y1  - 2014
A1  - T. Paiva
A1  - A. Chakraborty
A1  - J.P. Reiter
A1  - A.E. Gelfand
VL  - 33
ER  -