[Agresti, A. 2002. Categorical Data Analysis. New York: Wiley.10.1002/0471249688]Search in Google Scholar
[Agresti, A. and B.A. Coull. 1998. “Approximate is Better than “Exact” for Interval Estimation of Binomial Proportions.” The American Statistician 52: 119–126. Doi: http://dx.doi.org/10.1080/00031305.1998.10480550.10.1080/00031305.1998.10480550]Open DOISearch in Google Scholar
[Agresti, A. and D.B. Hitchcock. 2005. “Bayesian Inference for Categorical Data Analysis.” Statistical Methods and Applications 14: 297–330. Doi: http://dx.doi.org/10.1007/s10260-005-0121-y.10.1007/s10260-005-0121-y]Open DOISearch in Google Scholar
[Arentze, T., H. Timmermans, and F. Hofman. 2007. “Creating Synthetic Household Populations: Problems and Approach.” Journal of the Transportation Research Board, 2014, 85–91. Doi: http://dx.doi.org/10.3141/2014-11.10.3141/2014-11]Open DOISearch in Google Scholar
[Ballas, D., G. Clarke, D. Dorling, H. Eyre, B. Thomas, and D. Rossiter. 2005. “Simbritain: A Spatial Microsimulation Approach to Population Dynamics.” Population, Space and Place 11: 13–34. Doi: http://dx.doi.org/10.1002/psp.351.10.1002/psp.351]Open DOISearch in Google Scholar
[Barthélemy, J. and T. Suesse. 2016. “mipfp: Multidimensional Iterative Proportional Fitting and Alternative Models. R package version 3.1.” Available from: http://CRAN.R-project.org/package=mipfp.]Search in Google Scholar
[Barthélemy, J. and P.L. Toint. 2013. “Synthetic Population Generation without a Sample.” Transportation Science 47: 266–279. Doi: http://dx.doi.org/10.1287/trsc.1120.0408.10.1287/trsc.1120.0408]Open DOISearch in Google Scholar
[Beckman, R., K. Baggerly, and M. McKay. 1996. “Creating Synthetic Baseline Populations.” Transportation Research Part A: Policy and Practice 30: 415–429. Doi: http://dx.doi.org/10.1016/0965-8564(96)00004-3.10.1016/0965-8564(96)00004-3]Open DOISearch in Google Scholar
[Bergsma, W., M. Croon, and J. Hagenaars. 2009. Marginal Models for Dependent, Clustered and Longitudinal Categorical Data. New York: Springer.]Search in Google Scholar
[Causey, B.D. 1983. “Estimation of Proportions for Multinomial Contingency Tables Subject to Marginal Constraints.” Communications in Statistics-Theory and Methods 12: 2581–2587. Doi: http://dx.doi.org/10.1080/03610928308828624.10.1080/03610928308828624]Open DOISearch in Google Scholar
[De Campos, C.P. and A. Benavoli. 2011. “Inference with Multinomial Data: Why to Weaken the Prior Strength.” In IJCAI Proceedings-International Joint Conference on Artificial Intelligence, Barcelona, Catalonia, Spain July 16–22, 2011, Volume 22, pp. 2107. Available at: http://www.aaai.org/ocs/index.php/IJCAI/IJCAI11/paper/view/3292.]Search in Google Scholar
[Deming, W. and F. Stephan. 1940. “On a Least Squares Adjustment of a Sampled Frequency Table when the Expected Marginal Totals are Known.” Annals of Mathematical Statistics 11: 367–484. Available at: http://www.jstor.org/stable/2235722.10.1214/aoms/1177731829]Open DOISearch in Google Scholar
[Deville, J., C. Särndal, and O. Sautory. 1991. “Raking Procedures in Survey Sampling.” Journal of the American Statistical Association 86: 87–95.10.1080/01621459.1992.10475217]Search in Google Scholar
[Farooq, B., M. Bierlaire, R. Hurtubia, and G. Flotterod. 2013. “Simulation Based Population Synthesis.” Transportation Research Part B: Methodological 58: 243–263. Doi: http://dx.doi.org/10.1016/j.trb.2013.09.012.10.1016/j.trb.2013.09.012]Open DOISearch in Google Scholar
[Fienberg, S. 1970. “An Iterative Procedure for Estimation in Contingency Tables.” Annals of Mathematical Statistics 41: 907–917. Available at: http://www.jstor.org/stable/2239244.10.1214/aoms/1177696968]Open DOISearch in Google Scholar
[Gange, S.J. 1995. “Generating Multivariate Categorical Variates Using the Iterative Proportional Fitting Algorithm.” American Statistician 49: 134–138. Available at: http://www.tandfonline.com/doi/abs/10.1080/00031305.1995.10476130.10.1080/00031305.1995.10476130]Open DOISearch in Google Scholar
[Gargiulo, F., S. Ternes, S. Huet, and G. Deffuant. 2010. “An Iterative Approach for Generating Statistically Realistic Populations of Households.” PLOS ONE 5(1), e8828. Doi: http://dx.doi.org/10.1371/journal.pone.0008828.10.1371/journal.pone.0008828280974320107505]Open DOISearch in Google Scholar
[Geard, N., J. McCaw, A. Dorin, K. Korb, and J. McVernon. 2013. “Synthetic Population Dynamics: A Model of Household Demography.” Journal of Artificial Societies and Social Simulation 16(1): 1–23. Doi: http://dx.doi.org/10.18564/jasss.2098.10.18564/jasss.2098]Open DOISearch in Google Scholar
[Gelman, A., J. Carlin, H. Stern, and D. Rubin. 2003. Bayesian Data Analysis (2nd ed.). New York: Chapman and Hall/CRC Press.10.1201/9780429258480]Search in Google Scholar
[Harland, K., A. Heppenstall, D. Smith, and M. Birkin. 2012. “Creating Realistic Synthetic Populations at Varying Spatial Scales: A Comparative Critique of Population Synthesis Techniques.” Journal of Artificial Societies and Social Simulation 15: 1–24. Doi: http://dx.doi.org/10.18564/jasss.1909.10.18564/jasss.1909]Open DOISearch in Google Scholar
[Huynh, N., J. Barthelemy, and P. Perez. 2016. “A Heuristic Combinatorial Optimisation Approach to Synthesising a Population for Agent Based Modelling Purposes.” Journal of Artificial Societies and Social Simulation 19: 11. Doi: http://dx.doi.org/10.18564/jasss.3198.10.18564/jasss.3198]Open DOISearch in Google Scholar
[Ireland, C. and S. Kullback. 1968. “Contingency Tables with Given Marginals.” Biometrika 55: 179–199. Doi: https://doi.org/10.1093/biomet/55.1.179.10.1093/biomet/55.1.179]Open DOISearch in Google Scholar
[Jeffeys, H. 1998. The Theory of Probability. Oxford: Oxford University Press.]Search in Google Scholar
[Lang, J. 1996. “Maximum Likelihood Methods for a Generalized Class of Loglinear Models.” Annals of Statistics 24: 726–752.10.1214/aos/1032894462]Open DOISearch in Google Scholar
[Lang, J. 2004. “Multinomial-Poisson Homogeneous Models for Contingency Tables.” Annals of Statistics 32: 340–383.10.1214/aos/1079120140]Search in Google Scholar
[Lang, J. 2005. “Homogeneous Linear Predictor Models for Contingency Tables.” Journal of the American Statistical Association 100: 121–134. Doi: http://dx.doi.org/10.1198/016214504000001042.10.1198/016214504000001042]Open DOISearch in Google Scholar
[Lang, J. and A. Agresti. 1994. “Simultaneously Modelling Joint and Marginal Distributions of Multivariate Categorical Responses.” Journal of the American Statistical Association 89: 625–632.10.1080/01621459.1994.10476787]Search in Google Scholar
[Lenormand, M. and G. Deffuant. 2013. “Generating a Synthetic Population of Individuals in Households: Sample-Free vs Sample-Based Methods.” Journal of Artificial Societies and Social Simulation 16: 1–16. Doi: http://dx.doi.org/10.18564/jasss.2319.10.18564/jasss.2319]Open DOISearch in Google Scholar
[Little, J. and M. Wu. 1991. “Models for Contingency Tables with Known Margins when Target and Sampled Population Differ.” Journal of the American Statistical Association 86: 87–95.10.1080/01621459.1991.10475007]Open DOISearch in Google Scholar
[Lu, H. and A. Gelman. 2003. “A Method for Estimating Design-Based Sampling Variances for Surveys with Weighting, Poststratification, and Raking.” Journal of Official Statistics 19: 133–151.]Search in Google Scholar
[Mosteller, F. 1968. “Association and Estimation in Contingency Tables.” Journal of the American Statistical Association 63: 1–28. Doi: http://dx.doi.org/10.2307/2283825.10.2307/2283825]Open DOISearch in Google Scholar
[Purcell, N. and L. Kish. 1980. “Postcensal Estimates for Local Areas (or Domains).” International Statistical Review 43: 3–18.10.2307/1402400]Search in Google Scholar
[Rubin, D. 1987. Multiple Imputation for Nonresponse in Surveys. New York: Wiley.10.1002/9780470316696]Search in Google Scholar
[Smith, D., G. Clarke, and K. Harland. 2005. “Improving the Synthetic Data Generation Process in Spatial Microsimulation Models.” Environment and Planning A 41: 1251–1268. Doi: https://doi.org/10.1068/a4147.10.1068/a4147]Open DOISearch in Google Scholar
[Smith, J. 1947. “Estimation of Linear Functions of Cell Proportions.” Annals of Mathematical Statistics 18: 231–254.10.1214/aoms/1177730440]Open DOISearch in Google Scholar
[Stephan, F. 1942. “Iterative Method of Adjusting Frequency Tables when Expected Margins are Known.” Annals of Mathematical Statistics 13(2): 166–178.10.1214/aoms/1177731604]Open DOISearch in Google Scholar
[Zhang, L. and R. Chambers. 2004. “Small Area Estimates for Cross-Classifications.” Journal of the Royal Statistical Society: Series B 66: 479–496. Doi: http://dx.doi.org/10.1111/j.1369-7412.2004.05266.x.10.1111/j.1369-7412.2004.05266.x]Open DOISearch in Google Scholar