@article {2599,
	title = {Itemwise conditionally independent nonresponse modeling for incomplete multivariate data},
	journal = {Biometrika },
	volume = {104},
	year = {2017},
	month = {01/2017},
	pages = {207-220},
	chapter = {207},
	abstract = {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.},
	keywords = {Loglinear model, Missing not at random, Missingness mechanism, Nonignorable, Nonparametric saturated, Sensitivity analysis},
	doi = {10.1093/biomet/asw063},
	url = {https://doi.org/10.1093/biomet/asw063},
	author = {M. Sadinle and J.P. Reiter}
}
@article {1737,
	title = {A nonparametric, multiple imputation-based method for the retrospective integration of data sets},
	journal = {Multivariate Behavioral Research},
	volume = {50},
	year = {2015},
	pages = {383-397},
	chapter = {383},
	doi = {10.1080/00273171.2015.1022641},
	url = {http://www.tandfonline.com/doi/full/10.1080/00273171.2015.1022641},
	author = {M.M. Carrig and D. Manrique-Vallier and K. Ranby and J.P. Reiter and R. Hoyle}
}
@article {1575,
	title = {Semi-parametric selection models for potentially non-ignorable attrition in panel studies with refreshment samples},
	journal = {Political Analysis},
	volume = {23},
	year = {2015},
	pages = {92-112},
	chapter = {92},
	url = {http://pan.oxfordjournals.org/cgi/reprint/mpu009?\%20ijkey=joX8eSl6gyIlQKP\&keytype=ref},
	author = {Y. Si and J.P. Reiter and D.S. Hillygus}
}
@article {2185,
	title = {Stop or continue data collection: A nonignorable missing data approach for continuous variables},
	journal = {ArXiv},
	year = {2015},
	month = {11/2015},
	abstract = {We present an approach to inform decisions about nonresponse followup sampling. The basic idea is (i) to create completed samples by imputing nonrespondents{\textquoteright} 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{\textquoteright} data, and adjust the probabilities of the mixture components to generate nonrespondents{\textquoteright} distributions with desired features. We illustrate the approaches using data from the 2007 U. S. Census of Manufactures. },
	keywords = {Methodology},
	url = {http://arxiv.org/abs/1511.02189},
	author = {T. Paiva and J.P. Reiter}
}
@article {Manrique-Vallierforthcoming,
	title = {Bayesian multiple imputation for large-scale categorical data with structural zeros},
	journal = {Survey Methodology},
	volume = {40},
	year = {2014},
	month = {06/2014},
	pages = {125-134},
	url = {http://www.stat.duke.edu/~jerry/Papers/SurvMeth14.pdf},
	author = {D. Manrique-Vallier and J.P. Reiter}
}
@inbook {1576,
	title = {Disclosure risk evaluation for fully synthetic data},
	booktitle = {Privacy in Statistical Databases},
	volume = {8744},
	year = {2014},
	pages = {185-199},
	publisher = {Springer},
	organization = {Springer},
	address = {Heidelberg},
	author = {J. Hu and J.P. Reiter and Q. Wang}
}
@article {Paivaforthcoming,
	title = {Imputation of confidential data sets with spatial locations using disease mapping models},
	journal = {Statistics in Medicine},
	volume = {33},
	year = {2014},
	pages = {1928-1945},
	author = {T. Paiva and A. Chakraborty and J.P. Reiter and A.E. Gelfand}
}