Synthetic data methods
1.  Reiter, J. P. (2002)  Satisfying disclosure restrictions with synthetic data sets.  Journal of Official Statistics, 18, 531-544.
2.  Raghunathan, T. E., Reiter, J. P.,  and Rubin, D. B. (2003)  Multiple imputation for statistical disclosure limitation.  Journal of Official Statistics, 19, 1-16.
3.  Reiter, J. P. (2003)  Inference for partially synthetic, public use microdata sets. Survey Methodology, 29, 181-188.
4.  Reiter, J. P. (2004) New approaches to data dissemination: A glimpse into the future (?).  Chance, 17:3 (Summer 2004), 12 - 16.
5.  Reiter, J. P. (2004)  Simultaneous use of multiple imputation for missing data and disclosure limitation.  Survey Methodology 30, 235 - 242.
6.  Reiter, J. P. (2005)  Releasing multiply-imputed, synthetic public use microdata: An illustration and empirical study.  Journal of the Royal Statistical Society, Series A, 168, 185 - 205.
7.  Reiter, J. P. (2005)  Significance tests for multi-component estimands from multiply-imputed, synthetic microdata. Journal of Statistical Planning and Inference, 131, 365 - 377.
8.  Reiter, J. P. (2005)  Using CART to generate partially synthetic public use microdata.  Journal of Official Statistics, 21, 441 - 462.
9.  Mitra, R. and Reiter, J. P. (2006), Adjusting survey weights when altering identifying design variables via synthetic data, in Privacy in Statistical Databases 2006, Lecture Notes in Computer Science, New York: Springer-Verlag, 177 - 188
10.  Reiter, J. P. and Raghunathan, T. E. (2007), The multiple adaptations of multiple imputation, Journal of the American Statistical Association, 102, 1462 - 1471.
11.  Reiter, J. P. (2008), Selecting the number of imputed datasets when using multiple imputation for missing data and disclosure limitation, Statistics and Probability Letters, 78, 15 - 20.
12.  Drechsler, J. and Reiter, J. P. (2008), Accounting for intruder uncertainty due to sampling when estimating identification disclosure risks in partially synthetic data, Privacy in Statistical Databases (Lecture Notes in Computer Science 5262), ed. J. Domingo-Ferrer and Y. Saygin, Springer, 227 - 238.
13.  Kohnen C. N. and Reiter J. P. (2009), Multiple imputation for combining confidential data owned by two agencies, Journal of the Royal Statistical Society, Series A, 172, 511 - 528.
14.  Reiter, J. P. (2009), Using multiple imputation to integrate and disseminate confidential microdata, International Statistical Review, 77, 179 - 195.
15.  Reiter, J. P. and Mitra, R. (2009), Estimating risks of identification disclosure in partially synthetic data, Journal of Privacy and Confidentiality, 1.1, 99 - 110.
16. Drechsler, J. and Reiter, J. P. (2009), Disclosure risk and data utility for partially synthetic data: An empirical study using the German IAB Establishment Survey, Journal of Official Statistics, 25, 589 - 603.
17. Reiter, J. P. (2009), Multiple imputation for disclosure limitation: Future research challenges, Journal of Privacy and Confidentiality, 1:2, Article 7.
18.  Reiter, J. P. and Drechsler, J. (2010), Two stage multiple imputation to protect confidentiality, Statistica Sinica, 20, 405 - 422.
19. Caiola, G. and Reiter, J. P. (2010), Random forests for generating partially synthetic, categorical data, Transactions on Data Privacy, 3:1, 27 - 42.
20. Kinney, S. K. and Reiter, J. P. (2010), Tests of multivariate hypotheses when using multiple imputation for missing data and partial synthesis. Journal of Official Statistics, 26, 301 - 315.
21. Kinney, S., Reiter, J. P., and Berger, J. O. (2010), Model selection when multiple imputation is used to protect confidentiality in public use data, Journal of Privacy and Confidentiality, 2, Article 2.
22. Drechsler, J. and Reiter, J. P. (2010), Sampling with synthesis: A new approach to releasing public use microdata samples of census data, Journal of the American Statistical Association, 105, 1347 - 1357.
23. Reiter, J. P. (2011), Data confidentiality, Wiley Interdisciplinary Reviews: Computational Statistics, 3, 450 - 456.
24. Kinney, S. K., Reiter, J. P., Reznek, A. P., Miranda, J., Jarmin, R. S., and Abowd, J. M. (2011), Towards unrestricted public use business microdata: The synthetic Longitudinal Business Database, International Statistical Review, 79, 363 - 384.
25. Drechsler, J. and Reiter, J. P. (2011), An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets, Computational Statistics and Data Analysis, 55, 3232 - 3243.
26. Drechsler, J. and Reiter, J. P. (2012), Combining synthetic data with subsampling to create public use microdata files for large scale surveys, Survey Methodology, 38, 73 - 79.
27. Wang, H. and Reiter, J. P. (2012), Multiple imputation for sharing precise geographies in public use data, Annals of Applied Statistics, 6, 229 - 252.
28. Reiter, J. P. and Kinney, S. K. (2012), Inferentially valid, partially synthetic data: Generating from posterior predictive distributions not necessary, Journal of Official Statistics, 28, 583 - 590.
29. Burgette, L. F. and Reiter, J. P. (2013), Multiple-shrinkage multinomial probit models with applications to simulating geographies in public use data, Bayesian Analysis, 8, 453 - 478.
30. Paiva, T., Chakraborty, A., Reiter, J. P., Gelfand, A. E., (2014), Imputation of confidential data sets with spatial locations using disease mapping models, Statistics in Medicine, 33, 1928 - 1945.
31. Kinney, S. K., Reiter, J. P., and Miranda, J. (2014), SynLBD 2.0: Improving the Synthetic Longitudinal Business Database, Statistical Journal of the International Association for Official Statistics, 30, 129 - 135.
32. Hu, J., Reiter, J. P., and Wang, Q. (2014), Disclosure risk evaluation for fully synthetic data, in Privacy in Statistical Databases, edited by J. Domingo-Ferrer, Lecture Notes in Computer Science 8744, Heidelberg: Springer, 185 - 199.
33.  Reiter, J. P., Wang, Q., and Zhang, B. (2014), Bayesian estimation of disclosure risks in multiply imputed, synthetic data, Journal of Privacy and Confidentiality, 6:1, Article 2.
34.  Quick, H., Holan, S. H., Wikle, C. K., and Reiter, J. P. (2015), Bayesian marked point process modeling for generating fully synthetic public use data with point-referenced geography, Spatial Statistics, 14, 439 - 451.
35.  McClure, D. and Reiter, J. P. (2016), Assessing disclosure risks for synthetic data with arbitrary intruder knowledge, Statistical Journal of the International Association of Official Statistics, 32, 109 - 126..
36.  Wei, L. and Reiter, J. P. (2016), Releasing synthetic magnitude microdata constrained to fixed marginal totals, Statistical Journal of the International Association of Official Statistics, 32, 95 - 108.
37. Cronin, K. A., Feuer, R, Liu, B., Reiter, J. P., Yu, M. and Zhu, L. (forthcoming), "Protecting confidentiality in cancer registry data with geographic identifiers," American Journal of Epidemiology.
38. Hu, J., Reiter, J. P., and Wang, Q. (forthcoming), "Dirichlet process mixture models for modeling and generating synthetic versions of nested categorical data," Bayesian Analysis.
39. Kim, H. J., Reiter, J. P., and Karr, A. F. (forthcoming), "Simultaneous edit-imputation and disclosure limitation for business establishment data," Journal of Applied Statistics.

Assessing disclosure risk and data utility
1.  Reiter, J. P. (2005)   Estimating risks of identification disclosure for microdata.  Journal of the American Statistical Association, 100, 1103 - 1113.
2.  Karr, A. F., Kohnen, C. N., Oganian, A., Reiter, J. P. and Sanil, A. P. (2006), A framework for evaluating the utility of data altered to protect confidentiality, The American Statistician, 60, 224 - 232.
3.  Woo, M., Reiter, J. P., Oganian, A., Karr, A. F.  (2009), Global measures of data utility for microdata masked for disclosure limitation, Journal of Privacy and Confidentiality, 1.1, 111 - 124.
4.  Reiter J.P., Oganian A., and Karr AF (2009), Verification servers: enabling analysts to assess the quality of inferences from public use data. Computational Statistics and Data Analysis, 53, 1475 - 1482.
5.  Reiter, J. P. and Kinney, S. K. (2011), Sharing confidential data for research purposes: A primer [invited commentary], Epidemiology, 22, 632 - 635.
6.  Reiter, J. P. (2012), Statistical approaches to protecting confidentiality for microdata and their effects on the quality of statistical inferences, Public Opinion Quarterly, 76, 163 - 181.
7.  McClure, D. and Reiter, J. P. (2012), Towards providing automated feedback on the quality of inferences from synthetic datasets, Journal of Privacy and Confidentiality, 4:1, Article 8.
8.   McClure, D. and Reiter, J. P. (2012), Differential privacy and statistical disclosure risk measures: An illustration with binary synthetic data," Transactions on Data Privacy, 5:3, 535 - 552.
9.  Manrique-Vallier, D. and Reiter, J. P. (2012), Estimating identification disclosure risk using mixed membership models, Journal of the American Statistical Association, 107, 1385 - 1394.
10. Karr, A. F. and Reiter, J. P. (2014), Using statistics to protect privacy, in Privacy, Big Data, and the Public Good: Frameworks for Engagement, edited by J. Lane, V. Stodden, S. Bender, and H. Nissenbaum, Cambridge University Press, 276 - 295.
11.  Manrique-Vallier, D. and Reiter, J. P. (2014), Bayesian estimation of discrete multivariate latent structure models with structural zeros, Journal of Computational and Graphical Statistics, 23, 1061 - 1079.
12.  Kim, H. J., Karr, A. F., and Reiter, J. P. (2015), Statistical disclosure limitation in the presence of edit rules, Journal of Official Statistics, 31, 121 - 138.
13. Chen, Y., Machanavajjhala, A., Reiter, J. P., and Barrientos, A. F. (forthcoming), "Differentially private regression diagnostics," IEEE International Conference on Data Mining 2016 Proceedings.

Remote access and secure computation
1.  Reiter, J. P. (2003) Model diagnostics for remote-access regression servers. Statistics and Computing, 13, 371-380.
2.  Karr, A. F., Lin, X., Sanil, A. P. and Reiter, J. P. (2004), Analysis of integrated data without data integration, Chance, 17:3 (Summer 2004), 27 - 30.
3.  Sanil, A. P., Karr, A. F., Lin, X., and Reiter, J. P. (2004), Privacy preserving regression modelling via distributed computation, Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 677-682 (peer reviewed).
4.  Gomatam, S., Karr, A. F., Reiter, J. P., Sanil, A. (2005) Data dissemintation and disclosure limitation in a world without microdata: A risk-utility framework for remote access servers. Statistical Science, 20, 163 - 177.
5.  Karr, A. F., Lin, X., Sanil, A. P., and Reiter, J. P. (2005), Secure regressions on distributed databases, Journal of Computational and Graphical Statistics, 14, 263 - 279.
6.  Karr, A. F., Feng, J., Lin, X., Sanil, A. P., Young, S. S., and Reiter, J. P. (2005), Secure analysis of distributed chemical databases without data integration, Journal of Computer-Aided Molecular Design, 19, 739 - 747.
7.  Reiter, J. P. and Kohnen, C. N. (2005)  Categorical data regression diagnostics for remote servers. Journal of Statistical Computation and Simulation, 75, 889 - 903.
8.  Karr, A. F., Lin, X., Sanil, A. P., and Reiter, J. P. (2006), Secure statistical analysis of distributed databases, in Statistical Methods in Counterterrorism: Game Theory, Modeling, Syndromic Surveillance, and Biometric Authentication. Edited by A. Wilson, G. Wilson, and D. Olwell.  New York: Springer, 237 - 262.
9.  Ghosh, J., Reiter, J.P. and Karr, A. F. (2007), Secure computation with horizontally partitioned data using adaptive regression splines, Computational Statistics and Data Analysis, 51, 5813 - 5820.
10.  Karr, A. F., Fulp, W. J., Vera, F., Young, S. S., Lin, X., and Reiter, J. P. (2007), Secure, privacy-preserving analysis of distributed databases, Technometrics, 49, 335 - 345.
11.  Karr, A. F., Lin, X., Reiter, J. P. and Sanil, A. P. (2009), Privacy preserving analysis of vertically partitioned data using secure matrix protocols, Journal of Official Statistics, 25, 125 - 138.
12.  Ghosh, J. and Reiter, J. P. (2013), Secure Bayesian model averaging for horizontally partitioned data, Statistics and Computing, 23, 311 - 322.

Missing data methods
1.  Reiter, J. P., Raghunathan, T. E., and Kinney, S. (2006), The importance of modeling the sampling design in multiple imputation for missing data, Survey Methodology, 32.2, 143 - 150.
2.  Reiter, J. P. (2007), Small-sample degrees of freedom for multi-component significance tests with multiple imputation for missing data, Biometrika, 94, 502 - 508.
3.  Reiter, J. P. (2008), Multiple imputation when records used for imputation are not used or disseminated for analysis, Biometrika, 95, 933 - 946.
4.  Kinney, S. K. and Reiter, J. P. (2009), Inferences for two stage multiple imputation for nonresponse, Journal of Statistical Theory and Practice, 3, 307 - 318.
5.  Marchenko, Y. V. and Reiter, J. P. (2009), Improved degrees of freedom for multivariate significance tests obtained from multiply-imputed, small sample data, The Stata Journal, 9, 388 - 397.
6.  Burgette, L. and Reiter, J. P. (2010), Multiple imputation via sequential regression trees, American Journal of Epidemiology, 172, 1070 - 1076.
7.  Zhou, X. and Reiter, J. P. (2010), A note on Bayesian inference after multiple imputation, The American Statistician, 64, 159 - 163.
8.  Si, Y. and Reiter, J. P. (2011), A comparison of posterior simulation and inference by combining rules for multiple imputation, Journal of Statistical Theory and Practice, 5, 335 - 347.
9.  Mitra, R. and Reiter, J. P. (2011), Estimating propensity scores with missing covariate data using general location mixture models, Statistics in Medicine, 30, 627 - 641.
10.  Reiter, J. P. (2012), Bayesian finite population imputation for data fusion, Statistica Sinica, 22, 795 - 811.
11.  Burgette L. F. and Reiter, J. P. (2012), Nonparametric Bayesian multiple imputation for missing data due to mid-study switching of measurement methods, Journal of the American Statistical Association, 107, 439 - 449.
12.  Deng, Y., Hillygus, D. S., Reiter, J. P., Si, Y., and Zheng, S. (2013), Handling attrition in longitudinal studies: The case for refreshment samples, Statistical Science, 22, 238 - 256.
13.  Si, Y. and Reiter, J. P. (2013), Nonparametric Bayesian multiple imputation for incomplete categorical variables in large-scale assessment surveys, Journal of Educational and Behavioral Statistics, 38, 499 - 521.
14.  Hu, J., Mitra, R., and Reiter, J. P. (2013), Are Independent Draws Necessary for Multiple Imputation?, The American Statistician, 67, 143 - 149.
15.  Kim, H. J., Reiter, J. P., Wang, Q., Cox, L. H., and Karr, A. F. (2014) Multiple imputation of missing or faulty values under linear constraints, Journal of Business and Economic Statistics, 32, 375 - 386.
16.  Manrique-Vallier, D. and Reiter, J. P. (2014), Bayesian multiple imputation for large scale categorical data with structural zeros," Survey Methodology, 40, 125 - 134.
17.  Si, Y., Reiter, J. P., and Hillygus, D. S. (2015), Semi-parametric selection models for potentially non-ignorable attrition in panel studies with refreshment samples, Political Analysis, 23, 92 - 112.
18.  Carrig, M. M., Manrique-Vallier, D., Ranby, K., Reiter, J. P., and Hoyle, R. (2015), A multiple imputation-based method for the retrospective harmonization of data sets, Multivariate Behavioral Research, 50, 383 - 397.
19.  Schifeling, T., Cheng, C. Hillygus, S., and Reiter, J. P., (2015), Accounting for nonignorable unit nonresponse and attrition in panel studies with refreshment samples, Journal of Survey Statistics and Methodology, 3, 265 - 295.
20.   Kim, H. J., Cox, L., Karr, A. F., Reiter, J. P. and Wang, Q. (2015), Simultaneous edit-imputation for continuous microdata, Journal of the American Statistical Association, 110, 987 - 999.
21.  Si, Y., Reiter, J. P., and Hillygus, D. S. (2016), Bayesian latent pattern mixture models for handling attrition in panel studies with refreshment samples, Annals of Applied Statistics, 10, 118 - 143.
22.  Mitra, R. and Reiter, J. P. (2016), A comparison of two methods of estimating propensity scores after multiple imputation, Statistical Methods in Medical Research, 25, 188 - 204.
23.  Murray, J. S. and Reiter, J. P. (forthcoming) Multiple imputation of missing categorical and continuous values via Bayesian mixture models with local dependence, Journal of the American Statistical Association.
24. De Yoreo, M., Reiter, J. P., and Hillygus, D. S. (forthcoming), "Nonparametric Bayesian models with focused clustering for mixed ordinal and nominal data," Bayesian Analysis.
25. Manrique-Vallier, D. and Reiter, J. P. (forthcoming), "Bayesian simultaneous edit and imputation for multivariate categorical data," Journal of the American Statistical Association.
26. Sadinle, M. and Reiter, J. P. (forthcoming), "Itemwise conditionally independent nonresponse modeling for incomplete multivariate data," Biometrika.
27. White, T. K., Reiter, J. P. and Petrin, A. (forthcoming) "Imputation in U. S. manufacturing data and its implications for productivity dispersion," Review of Economics and Statistics.
28. Akande, O., Li, F., and Reiter, J. P. (forthcoming), "An empirical comparison of multiple imputation methods for categorical data," The American Statistician.

Analysis of complex data and causal inference
1.  Reiter, J. P. (2000), Using statistics to determine causal relationships, The American Mathematical Monthly, 107, 24-32.
2.  Reiter, J. P. (2000), Borrowing strength when explicit data pooling is prohibited, Journal of Official Statistics, 16, 296-319.
3.  Hill, J. L., Reiter, J. P., Zanutto, E. (2004), A comparison of experimental and observational data analyses, In Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives, edited by A. Gelman and X. Meng. New York: Wiley, 49 - 60.
4.  Reiter, J., Zanutto, E., and Hunter, L. (2005),, Analytical modeling in complex surveys of work practices,  Industrial Labor Relations Review, 59, 82-100.
5.  Hill, J. L. and Reiter, J. P. (2006), Interval estimation of treatment effects when using propensity score matching,  Statistics in Medicine, 25:13, 2230 - 2256.
6.  Woo, M. J., Reiter, J. P., and Karr, A. F. (2008), Estimation of propensity scores using generalized additive models, Statistics in Medicine, 27, 3806 - 3816.
7.  Burgette, L., Reiter, J. P., and Miranda, M. L. (2011), Exploratory quantile regression with many covariates: An application to adverse birth outcomes, Epidemiology, 22, 859 - 866.
8.  Schwartz, S., Li, F., and Reiter, J. P. (2012), Sensitivity analysis for unmeasured confounding in principal stratification, Statistics in Medicine, 31, 949 - 962.
9.  Burgette, L. F. and Reiter, J. P. (2012), Modeling adverse birth outcomes via confirmatory factor quantile regressions, Biometrics, 68, 92 - 100.
10.  Siddique, J., Reiter, J. P., Brincks, A., Gibbons, R., Crespi, C., and Brown, C. H. (2015), Multiple imputation for harmonizing non-commensurate measures in individual participant data meta-analysis, Statistics in Medicine, 34, 3399 - 3414.
11.  Schifeling, T. and Reiter, J. P. (2016), Incorporating marginal prior information in latent class models, Bayesian Analysis, 11, 499 - 518.
12.  Fosdick, B. K., De Yoreo, M. and Reiter, J. P. (forthcoming), Categorical data fusion using auxiliary data, Annals of Applied Statistics.

Data analysis
1.  Gu, B., Olejar, K., Reiter, J. P., Thor, K. B., and Dolber, P. C.  (2004), Inhibition of bladder activity by 5HT1 serotonin receptor antagonists in cats with chronic spinal cord injury, Journal of Pharmacology and Experimental Therapeutics, 310:3, 1266 - 1272.
2.  Gu, B., Reiter, J. P., Schwinn, D. A., Smith, M. P., Korstanje, C., Thor, K. B., and Dolber, P. C. (2004), Effects of alpha1-adrenergic receptor subtype selective antagonists on lower urinary tract function in rats with bladder outlet obstruction, Journal of Urology, 172, 758 - 762.
3.  Reiter, J. P. (2004), Should teams walk or pitch to Barry Bonds?  Baseball Research Journal, 32, 63-69.  (Citations and news articles)
4.  Orr, S. T., Blazer, D. G., James, S. A., and Reiter, J. P. (2007), Depressive symptoms and indicators of maternal health status during pregnancy, Journal of Women's Health, 16, 535 - 542.
5.  Orr, S. T., Blazer, D. G., James, S. A., and Reiter, J. P. (2007), Maternal prenatal pregnancy-related anxiety and spontaneous preterm birth in Baltimore, Maryland,Psychosomatic Medicine, 69, 566 - 570.
6.  Dolber, P. C., Gu, B., Zhang, X., Fraser, M. O., Thor, K. B., and Reiter, J. P. (2007) Activation of the external urethral sphincter central pattern generator by a 5-HT1A serotonin receptor agonist in rats with chronic spinal cord injury, American Journal of Physiology: Regulatory, Integrative, and Comparative Physiology, 292, R1699 - R1706.
7.  Gu, B., Thor, K. B., Reiter, J. P., and Dolber, P. C. (2007) Effect of 5HHT-1 serotonin receptor agonists on noxiously stimulated micturition in cats with chronic spinal cord injury, Journal of Urology, 177, 2381 - 2385.
8.  Kwiek, N. C. Halpin, M. J., Reiter, J. P., Hoeffler, L. A., and Schwartz-Bloom, R. D. (2007), Pharmacology in the high school classroom,Science, 317 (September 28, 2007), 1871 - 1872 (with supplemental material).
9.  Orr, S. T., James, S. A., and Reiter, J. P. (2008), Unintended pregnancy and prenatal behaviors among urban, black women in Baltimore, Maryland: The Baltimore preterm birth study, Annals of Epidemiology, 18, 545 - 551.
10.  Montgomery, J., Cooper, A., Reiter, J. P, and Guan, S. (2008), A comparison of respondents and non-respondents on dimensions of political activity, International Journal of Public Opinion Research, 20, 494 - 506.
11.  Reiter J. P. (2008), Statistics in sports: Current and future research trends, STAtOR 9.2, 4 - 7.
12.  Miranda, M. L., Kim, D., Reiter, J. P., Overstreet, M. A., Maxson, P. (2009), Environmental contributors to the achievement gap, NeuroToxicology, 30, 1019 - 1024.
13.  Schwartz-Bloom, R. D., Halpin, M. J., and Reiter, J. P. (2011), Teaching high school chemistry in the context of pharmacology helps both teachers and students learn, Journal of Chemical Education, 88, 744 - 750.
15.  Petrin, A., Reiter, J. P., and White, T. K. (2011), The impact of plant-level resource reallocations and technical progress on U.S. macroeconomic growth, Review of Economic Dynamics, 14, 3 - 26. Also published as NBER Working Paper No. 16700.
16.  Orr, S. T., Reiter, J. P., James, S. A., and Orr, C. A. (2012), Maternal health prior to pregnancy and preterm birth among urban, low income black women in Baltimore: The Baltimore Preterm Birth Study. Ethnicity and Disease, 22, 85 - 89.
17.  Godin, E. A., Kwiek, N., Sikes, S. S., Halpin, M. J., Weinbaum, C. A., Burgette, L. F., Reiter, J. P., and Schwartz-Bloom, R. D. (2014), Alcohol pharmacology education partnership: Using chemistry and biology concepts to educate high school students about alcohol, Journal of Chemical Education, 91, 165 - 172.
18.  Grochowski, C. O., Cartmill, M., Reiter, J., Spaulding, J., Haviland, J., Valea, F., Thibodeau, P. L., McCorison, S., and Halperin, E. C. (2014), "Anxiety in first year medical students taking gross anatomy," Clinical Anatomy, 27, 835 - 838.