[Brozzi, A., A. Capotorti, and B. Vantaggi. 2012. “Incoherence Correction Strategies in Statistical Matching.” International Journal of Approximate Reasoning 53: 1124–1136. Doi: http://dx.doi.org/10.1016/j.ijar.2012.06.009.]Search in Google Scholar
[Conti, P.L., D. Marella, and M. Scanu. 2008. “Evaluation of Matching Noise for Imputation Techniques Based on Nonparametric Local Linear Regression Estimators.” Computational Statistics & Data Analysis 53: 354–365. Doi: http://dx.doi.org/10.1016/j.csda.2008.07.041.]Search in Google Scholar
[Conti, P.L., M. Di Zio, D. Marella, and M. Scanu. 2009. “Uncertainty Analysis in Statistical Matching.” Paper given at the First Italian Conference on Survey Methodology (ITACOSM09), June 10–12, 2009, Siena]Search in Google Scholar
[Conti, P.L., D. Marella, and M. Scanu. 2012. “Uncertainty Analysis in Statistical Matching.” Journal of Official Statistics 28: 69–88.]Search in Google Scholar
[Conti, P.L., D. Marella, and M. Scanu. 2013. “Uncertainty Analysis for Statistical Matching of Ordered Categorical Variables.” Computational Statistics & Data Analysis 68: 311–325. Doi: http://dx.doi.org/10.1016/j.csda.2013.07.004.]Search in Google Scholar
[Cain, M. 1994. “The Moment-generating Function of the Minimum of Bivariate Normal Random Variables.” The American Statistician 48: 124–125. Doi: http://dx.doi.org/10.1080/00031305.1994.10476039.]Search in Google Scholar
[Chambers, R.L. and R.G. Steel. 2001. “Simple Methods for Ecological Inference in 2 × 2 Tables.” Journal of the Royal Statistical Society Series A 164: 175–192. Doi: http://dx.doi.org/10.1111/1467-985X.00195.]Search in Google Scholar
[D’Orazio, M., M. Di Zio, and M. Scanu. 2006a. “Statistical Matching for Categorical Data: Displaying Uncertainty and Using Logical Constraints.” Journal of Official Statistics 22: 137–157.]Search in Google Scholar
[D’Orazio, M., M. Di Zio, and M. Scanu. 2006b. Statistical Matching: Theory and Practice. Chichester: Wiley.10.1002/0470023554]Search in Google Scholar
[Kadane, J.B. 1978. “Some Statistical Problems in Merging Data Files.” In 1978 Compendium of Tax Research, (pp. 159–171). Washington, D.C. Department of Treasury. (Reprinted in Journal of Official Statistics 17: 423–433.).]Search in Google Scholar
[King, G. 1997. A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data. Princeton: Princeton University Press.10.3886/ICPSR01132]Search in Google Scholar
[Koopmans, T. 1949. “Identification Problems in Economic Model Construction.” Econometrica 17: 125–144. Doi: http://dx.doi.org/10.2307/1905689.]Search in Google Scholar
[Lindley, D.V., A. Tversky, and R.V. Brown. 1979. “On the Reconciliation of Probability Assessments (incl. discussions).” Journal of the Royal Statistical Society Series A 142: 146–180. Doi: http://dx.doi.org/10.2307/2345078.]Search in Google Scholar
[Manski, C.F. 1995. Identification Problems in the Social Sciences. Cambridge, MA: Harvard University Press.]Search in Google Scholar
[Marella, D., P.L. Conti, and M. Scanu. 2008. “On the Matching Noise of Some Nonparametric Imputation Procedures.” Statistics and Probability Letters 78: 1593–1600. Doi: http://dx.doi.org/10.1016/j.spl.2008.01.020.]Search in Google Scholar
[Moriarity, C. and F. Scheuren. 2001. “Statistical Matching: A Paradigm for Assessing the Uncertainty in the Procedure.” Journal of Official Statistics 17: 407–422.]Search in Google Scholar
[Nadarajah, S. and S. Kotz. 2008. “Exact Distribution of the Max/Min of Two Gaussian Random Variables.” IEEE Transactions on Very Large Scale Integration (VLSI) Systems 16: 210–212. Doi: http://dx.doi.org/10.1109/TVLSI.2007.912191.]Search in Google Scholar
[Okner, B.A. 1972. “Constructing a New Microdata Base From Existing Microdatasets: the 1966 Merge File.” Annals of Economic and Social Measurement 1: 325–342.]Search in Google Scholar
[Patel, J.K., C.H. Kapadia, and D.B. Owen. 1976. Handbook of Statistical Distributions. New York: Marcel Dekker.]Search in Google Scholar
[Plackett, R.L. 1977. “The Marginal Totals of a 2 × 2 Table.” Biometrika 64: 37–42. Doi: http://dx.doi.org/10.1093/biomet/64.1.37.]Search in Google Scholar
[Purcell, N.J. and L. Kish. 1980. “Postcensal Estimates for Local Areas (or Domains).” International Statistical Review 48: 3–18. Doi: http://dx.doi.org/10.2307/1402400.]Search in Google Scholar
[Rässler, S. 2002. Statistical Matching: A Frequentist Theory, Practical Applications and Alternative Bayesian Approaches, Vol. 168 of Lecture Notes in Statistics. New York: Springer Verlag.10.1007/978-1-4613-0053-3_2]Search in Google Scholar
[Rässler, S. and H. Kiesl. 2009. “How Useful Are Uncertainty Bounds? Some Recent Theory With an Application to Rubin’s Causal Model.” In Proceedings of the 57th Sessions of the International Statistical Institute. (2009) CD-ROM. Durban, South Africa.]Search in Google Scholar
[Singh, A.C., H. Mantel, M. Kinack, and G. Rowe. 1993. “Statistical Matching: Use of Auxiliary Information as an Alternative to the Conditional Independence Assumption.” Survey Methodology 19: 57–79.]Search in Google Scholar
[Vantaggi, B. 2008. “Statistical Matching of Multiple Sources: A Look Through Coherence.” International Journal of Approximate Reasoning 49: 701–711. Doi: http://dx.doi.org/10.1016/j.ijar.2008.07.005.]Search in Google Scholar
[Wakefield, J. 2004. “Ecological Inference for 2 × 2 Tables (incl. discussions).” Journal of the Royal Statistical Society Series A 167: 385–445. Doi: http://dx.doi.org/10.1111/j.1467-985x.2004.02046.x.]Search in Google Scholar