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Cressie, N.. "Comparing and Selecting Predictors Predictors Using Local Criteria." In International Workshop on Recent Advances in Statistical Inference: Theory and Case Studies. Padua, Italy: International Workshop on Recent Advances in Statistical Inference: Theory and Case Studies, 2013.
Cressie, N., and R. L. Chambers. "Comment: Spatial sampling designs depend as much on “how much?” and “why?” as on “where?”." Bayesian Analysis (2015).
Cressie, N., and A. Zammit-Mangion. "Multivariate Spatial Covariance Models: A Conditional Approach." (2015), available at https://arxiv.org/abs/1504.01865.
Cressie, Noel, Scott H. Holan, and Christopher K. Wikle. NCRN Meeting Spring 2015: Training Undergraduates, Graduate Students, Postdocs, and Federal Agencies: Methodology, Data, and Science for Federal Statistics. NCRN Coordinating Office Preprint 1813:40179, 2015, available at http://hdl.handle.net/1813/40179.
Cressie, N., and E.L. Kang. "Hot enough for you? A spatial exploratory and inferential analysis of North American climate-change projections." Mathematical Geosciences (2015). DOI: 10.1007/s11004-015-9607-9, available at http://dx.doi.org/10.1007/s11004-015-9607-9.
Cressie, N.. Statistics for Spatio-Temporal Data, Invited One-Day Short Course at the U.S. Census Bureau., 2013.
Cressie, N., and R. L. Chambers. "Comment on Article by Ferreira and Gamerman." Bayesian Analysis 10, no. 3 (2015): 741-748. DOI: doi:10.1214/15-BA944B, available at http://projecteuclid.org/euclid.ba/1429880217.
Cressie, N., S. Burden, W. Davis, P. Krivitsky, P. Mokhtarian, T. Seusse, and A. Zammit-Mangion. "Capturing multivariate spatial dependence: Model, estimate, and then predict." Statistical Science 30, no. 2 (2015): 170-175. DOI: 10.1214/15-STS517, available at http://projecteuclid.org/euclid.ss/1433341474.
Cressie, N., and S. Burden. "Figures of merit for simultaneous inference and comparisons in simulation experiments." Stat 4, no. 1 (2015): 196-211. DOI: 10.1002/sta4.88, available at http://onlinelibrary.wiley.com/doi/10.1002/sta4.88/epdf.
Cressie, N., and S. Burden. "Evaluation of diagnostics for hierarchical spatial statistical models." In Geometry Driven Statistics, edited by I.L. Dryden and J.T. Kent, 241-256. Chinchester: Wiley, 2015, available at http://niasra.uow.edu.au/content/groups/public/@web/@inf/@math/documents/doc/uow169240.pdf.
Cressie, N.. Some Historical Remarks on Spatial Statistics, Spatio-Temporal Statistics, Reading Group, University of Missouri., 2013.
Crimi, N., and W. C. Eddy. "Top-Coding and Public Use Microdata Samples from the U.S. Census Bureau." Journal of Privacy and Confidentiality 6 (2014): 21-58, available at http://repository.cmu.edu/jpc/vol6/iss2/2/.
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Dalzell, N. M., and J. P. Reiter. Regression Modeling and File Matching Using Possibly Erroneous Matching Variables. ArXiv 1608.06309, 2016, available at http://arxiv.org/abs/1608.06309.
De Yoreo, M., and A. Kottas. "A Bayesian nonparametric Markovian model for nonstationary time series." Statistics and Computing (2016).
De Yoreo, M., J. P. Reiter, and D. S. Hillygus. "Nonparametric Bayesian models with focused clustering for mixed ordinal and nominal data." Bayesian Analysis (2015). DOI: 10.1214/16-BA1020.
Deal, C.E., A. Kirchner, A.L. Cordova-Cazar, L. Ellyne, and R.F. Belli. "Changing ‘Who’ or ‘Where’: Implications for Data Quality in the American Time Use Survey." In 70th Annual Conference of the American Association for Public Opinion Research (AAPOR). Hollywood, Florida, 2015, available at http://www.aapor.org/AAPORKentico/Conference/Recent-Conferences.aspx.
Deal, C., A.L. Cordova-Cazar, A. Countryman, A. Kirchner, and R.F. Belli. "Remembering where: A look at the American Time Use Survey." In Paper presented at the annual conference of the Midwest Association for Public Opinion Research. Chicago, IL, 2014, available at http://www.mapor.org/conferences.html.
Deng, Yiting, Sunshine D. Hillygus, Jerome P. Reiter, Yajuan Si, and Siyu Zheng. "Handling Attrition in Longitudinal Studies: The Case for Refreshment Samples." Statist. Sci. 28 (2013): 238-256. DOI: 10.1214/13-STS414, available at http://dx.doi.org/10.1214/13-STS414.
DeYoreo, Maria, J. P. Reiter, and D. S. Hillygus. "Nonparametric Bayesian models with focused clustering for mixed ordinal and nominal data." ArXiV, no. 1508.03758 (2015), available at http://arxiv.org/abs/1508.03758.
DeYoreo, Maria, and Jerome P. Reiter. Bayesian mixture modeling for multivariate conditional distributions. ArXiv 1606.04457, 2016, available at http://arxiv.org/abs/1606.04457.
DeYoreo, Maria, and Athanasios Kottas. A Bayesian nonparametric Markovian model for nonstationary time series. ArXiv 1601.04331, 2016, available at http://arxiv.org/abs/1601.04331.
DeYoreo, M., and A. Kottas. Bayesian Nonparametric Modeling for Multivariate Ordinal Regression. ArXiv 1408.1027, 2014, available at http://arxiv.org/abs/1408.1027.
DeYoreo, M., and A. Kottas. Modeling for Dynamic Ordinal Regression Relationships: An Application to Estimating Maturity of Rockfish in California. ArXiv 1507.01242, 2015, available at http://arxiv.org/abs/1507.01242.
Dunn, Abe, Eli Liebman, and Adam Shapiro. NCRN Meeting Fall 2014: Decomposing Medical-Care Expenditure Growth. NCRN Coordinating Office Preprint 1813:37411, 2014, available at http://hdl.handle.net/1813/37411.

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