Project Euclid

References

• Agresti, A. (1990). Categorical Data Analysis. Wiley, New York.
  Zentralblatt MATH: 0716.62001
  
• Albert, R. and Barabási, A.-L. (2002). Statistical mechanics of complex networks. Rev. Modern Phys. 74 47–97.
• Baldi, P., Brunak, S., Chauvin, Y., Andersen, C. A. and Nielsen, H. (2000). Assessing the accuracy of prediction algorithms for classification: An overview. Bioinformatics 16 412–424.
• Baltazar, C. S., Horth, R., Inguane, C., Sathane, I., César, F., Ricardo, H., Botão, C., Augusto, Â., Cooley, L., Cummings, B., Raymond, H. F. and Young, P. W. (2015). HIV prevalence and risk behaviors among Mozambicans working in South African mines. AIDS Behav. 19 59–67.
• Becker, R., Cáceres, R., Hanson, K., Isaacman, S., Loh, J. M., Martonosi, M., Rowland, J., Urbanek, S., Varshavsky, A. and Volinsky, C. (2013). Human mobility characterization from cellular network data. Commun. ACM 56 74–82.
• Besag, J. (1975). Statistical analysis of non-lattice data. J. R. Stat. Soc., Ser. D Stat. 24 179–195.
• Besag, J. (1977). Efficiency of pseudolikelihood estimation for simple Gaussian fields. Biometrika 64 616–618.
  Zentralblatt MATH: 0372.62067
  Digital Object Identifier: doi:10.1093/biomet/64.3.616
  
• Bhattacharya, A. and Dunson, D. B. (2012). Simplex factor models for multivariate unordered categorical data. J. Amer. Statist. Assoc. 107 362–377.
  Mathematical Reviews (MathSciNet): MR2949366
  Zentralblatt MATH: 1263.62097
  Digital Object Identifier: doi:10.1080/01621459.2011.646934
  
• Bishop, Y. M. M., Fienberg, S. E. and Holland, P. W. (1975). Discrete Multivariate Analysis: Theory and Practice. MIT Press, Cambridge, MA. With the collaboration of Richard J. Light and Frederick Mosteller.
  Zentralblatt MATH: 0332.62039
  
• Brockmann, D., Hufnagel, L. and Geisel, T. (2006). The scaling laws of human travel. Nature 439 462–465.
• Calabrese, F., Diao, M., Lorenzo, G. D., Ferreira Jr., J. and Ratti, C. (2013). Understanding individual mobility patterns from urban sensing data: A mobile phone trace example. Transp. Res., Part C, Emerg. Technol. 26 301–313.
• Canale, A. and Dunson, D. B. (2011). Bayesian kernel mixtures for counts. J. Amer. Statist. Assoc. 106 1528–1539.
  Zentralblatt MATH: 1233.62041
  Digital Object Identifier: doi:10.1198/jasa.2011.tm10552
  
• Cappé, O., Robert, C. P. and Rydén, T. (2003). Reversible jump, birth-and-death and more general continuous time Markov chain Monte Carlo samplers. J. R. Stat. Soc. Ser. B. Stat. Methodol. 65 679–700.
• Cheng, Y. and Lenkoski, A. (2012). Hierarchical Gaussian graphical models: Beyond reversible jump. Electron. J. Stat. 6 2309–2331.
  Mathematical Reviews (MathSciNet): MR3020264
  Zentralblatt MATH: 1335.62042
  Digital Object Identifier: doi:10.1214/12-EJS746
  Project Euclid: euclid.ejs/1354284421
  
• Clyde, M. and George, E. I. (2004). Model uncertainty. Statist. Sci. 19 81–94.
  Zentralblatt MATH: 1062.62044
  Digital Object Identifier: doi:10.1214/088342304000000035
  Project Euclid: euclid.ss/1089808274
  
• Dellaportas, P. and Forster, J. J. (1999). Markov chain Monte Carlo model determination for hierarchical and graphical log-linear models. Biometrika 86 615–633.
  Zentralblatt MATH: 0949.62050
  Digital Object Identifier: doi:10.1093/biomet/86.3.615
  
• Dellaportas, P. and Tarantola, C. (2005). Model determination for categorical data with factor level merging. J. R. Stat. Soc. Ser. B. Stat. Methodol. 67 269–283.
  Zentralblatt MATH: 1069.62049
  Digital Object Identifier: doi:10.1111/j.1467-9868.2005.00501.x
  
• Descombes, X., Minlos, R. and Zhizhina, E. (2009). Object extraction using a stochastic birth-and-death dynamics in continuum. J. Math. Imaging Vision 33 347–359.
  Mathematical Reviews (MathSciNet): MR2480967
  Digital Object Identifier: doi:10.1007/s10851-008-0117-y
  
• Dobra, A. and Lenkoski, A. (2011). Copula Gaussian graphical models and their application to modeling functional disability data. Ann. Appl. Stat. 5 969–993.
  Zentralblatt MATH: 1232.62046
  Digital Object Identifier: doi:10.1214/10-AOAS397
  Project Euclid: euclid.aoas/1310562213
  
• Dobra, A., Lenkoski, A. and Rodriguez, A. (2011). Bayesian inference for general Gaussian graphical models with application to multivariate lattice data. J. Amer. Statist. Assoc. 106 1418–1433.
  Mathematical Reviews (MathSciNet): MR2896846
  Zentralblatt MATH: 1234.62018
  Digital Object Identifier: doi:10.1198/jasa.2011.tm10465
  
• Dobra, A. and Massam, H. (2010). The mode oriented stochastic search (MOSS) algorithm for log-linear models with conjugate priors. Stat. Methodol. 7 240–253.
  Zentralblatt MATH: 1291.62066
  Digital Object Identifier: doi:10.1016/j.stamet.2009.04.002
  
• Dobra, A. and Mohammadi, R. (2018). Supplement to “Loglinear model selection and human mobility.” DOI:10.1214/18-AOAS1164SUPP.
• Dobra, A., Williams, N. E. and Eagle, N. (2015). Spatiotemporal detection of unusual human population behavior using mobile phone data. PLoS ONE 10 1–20.
• Dobra, A., Bärnighausen, T., Vandormael, A. and Tanser, F. (2017). Space-time migration patterns and risk of HIV acquisition in rural South Africa. AIDS 31 37–145.
• Donato, K. M. (1993). Current trends and patterns of female migration: Evidence from Mexico. Int. Migr. Rev. 27 748–771.
• Drton, M. and Maathuis, M. H. (2017). Structure learning in graphical modeling. Annu. Rev. Statist. Appl. 4 365–393.
• Dunson, D. B. and Xing, C. (2009). Nonparametric Bayes modeling of multivariate categorical data. J. Amer. Statist. Assoc. 104 1042–1051.
  Zentralblatt MATH: 1388.62151
  Digital Object Identifier: doi:10.1198/jasa.2009.tm08439
  
• Durand, J., Kandel, W., Parrado, E. A. and Massey, D. S. (1996). International migration and development in Mexican communities. Demography 33 249–264.
• Edwards, D. and Havránek, T. (1985). A fast procedure for model search in multidimensional contingency tables. Biometrika 72 339–351.
  Zentralblatt MATH: 0576.62067
  Digital Object Identifier: doi:10.1093/biomet/72.2.339
  
• Fienberg, S. E. (1970). The analysis of multidimensional contingency tables. Ecology 51 419–433.
• Fienberg, S. E. (1980). The Analysis of Cross-Classified Categorical Data, 2nd ed. MIT Press, Cambridge, MA.
  Zentralblatt MATH: 0499.62049
  
• Fienberg, S. E. and Rinaldo, A. (2007). Three centuries of categorical data analysis: Log-linear models and maximum likelihood estimation. J. Statist. Plann. Inference 137 3430–3445.
  Mathematical Reviews (MathSciNet): MR2363267
  Zentralblatt MATH: 1119.62053
  Digital Object Identifier: doi:10.1016/j.jspi.2007.03.022
  
• Fienberg, S. E. and Rinaldo, A. (2012). Maximum likelihood estimation in log-linear models. Ann. Statist. 40 996–1023.
  Zentralblatt MATH: 1274.62389
  Digital Object Identifier: doi:10.1214/12-AOS986
  Project Euclid: euclid.aos/1342625459
  
• Gamal-Eldin, A., Descombes, X. and Zerubia, J. (2010). Multiple birth and cut algorithm for point process optimization. In 2010 Sixth International Conference on Signal-Image Technology and Internet-Based Systems (SITIS) 35–42. IEEE, Los Alamitos, CA.
• Gamal-Eldin, A., Descombes, X., Charpiat, G. and Zerubia, J. (2011). A fast multiple birth and cut algorithm using belief propagation. In 2011 18th IEEE International Conference on Image Processing 2813–2816. IEEE, Los Alamitos, CA.
• Gonzalez, M. C., Hidalgo, C. A. and Barabasi, A.-L. (2008). Understanding individual human mobility patterns. Nature 453 779–782.
• Green, P. J. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82 711–732.
  Zentralblatt MATH: 0861.62023
  Digital Object Identifier: doi:10.1093/biomet/82.4.711
  
• Guerzhoy, M. and Hertzmann, A. (2014). Learning latent factor models of travel data for travel prediction and analysis. In Advances in Artificial Intelligence. Lecture Notes in Computer Science 8436 131–142. Springer, Cham.
• Harris, J. R. and Todaro, M. P. (1970). Migration, unemployment and development: A two-sector analysis. Am. Econ. Rev. 60 126–142.
• Hoff, P. D. (2008). Multiplicative latent factor models for description and prediction of social networks. Comput. Math. Organ. Theory 15 Art. ID 261.
• Höfling, H. and Tibshirani, R. (2009). Estimation of sparse binary pairwise Markov networks using pseudo-likelihoods. J. Mach. Learn. Res. 10 883–906.
  Mathematical Reviews (MathSciNet): MR2505138
  Zentralblatt MATH: 1245.62121
  
• Højsgaard, S., Edwards, D. and Lauritzen, S. (2012). Graphical Models with R. Springer, New York.
  Mathematical Reviews (MathSciNet): MR2905395
  
• Imai, K. (2017). Quantitative Social Science: An Introduction. Princeton Univ. Press, Princeton, NJ.
• Jones, B., Carvalho, C., Dobra, A., Hans, C., Carter, C. and West, M. (2005). Experiments in stochastic computation for high-dimensional graphical models. Statist. Sci. 20 388–400.
  Zentralblatt MATH: 1130.62408
  Digital Object Identifier: doi:10.1214/088342305000000304
  Project Euclid: euclid.ss/1137076659
  
• Jurdak, R., Zhao, K., Liu, J., AbouJaoude, M., Cameron, M. and Newth, D. (2015). Understanding human mobility from Twitter. PLoS ONE 10 1–16.
• Kunihama, T. and Dunson, D. B. (2013). Bayesian modeling of temporal dependence in large sparse contingency tables. J. Amer. Statist. Assoc. 108 1324–1338.
  Zentralblatt MATH: 1283.62120
  Digital Object Identifier: doi:10.1080/01621459.2013.823866
  
• Lauritzen, S. L. (1996). Graphical Models. Oxford Statistical Science Series 17. The Clarendon Press, Oxford Univ. Press, New York.
  Mathematical Reviews (MathSciNet): MR1419991
  
• Leetaru, K., Wang, S., Cao, G., Padmanabhan, A. and Shook, E. (2013). Mapping the global Twitter heartbeat: The geography of Twitter. First Monday 18. Available at http://firstmonday.org/ojs/index.php/fm/article/view/4366/3654.
• Lenkoski, A. and Dobra, A. (2011). Computational aspects related to inference in Gaussian graphical models with the G-Wishart prior. J. Comput. Graph. Statist. 20 140–157. Supplementary material available online.
• Letac, G. and Massam, H. (2012). Bayes factors and the geometry of discrete hierarchical loglinear models. Ann. Statist. 40 861–890.
  Zentralblatt MATH: 1274.62391
  Digital Object Identifier: doi:10.1214/12-AOS974
  Project Euclid: euclid.aos/1338515140
  
• Madigan, D. and Raftery, A. E. (1994). Model selection and accounting for model uncertainty in graphical models using Occam’s window. J. Amer. Statist. Assoc. 89 1535–1546.
  Zentralblatt MATH: 0814.62030
  Digital Object Identifier: doi:10.1080/01621459.1994.10476894
  
• Madigan, D. and York, J. (1995). Bayesian graphical models for discrete data. Int. Stat. Rev. 63 215–232.
  Zentralblatt MATH: 0834.62003
  Digital Object Identifier: doi:10.2307/1403615
  
• Madigan, D. and York, J. C. (1997). Bayesian methods for estimation of the size of a closed population. Biometrika 84 19–31.
  Zentralblatt MATH: 0887.62029
  Digital Object Identifier: doi:10.1093/biomet/84.1.19
  
• Madigan, D., Raftery, A. E., Volinsky, C. and Hoeting, J. (1996). Bayesian model averaging. In Proceedings of the AAAI Workshop on Integrating Multiple Learned Models 77–83.
• Massam, H., Liu, J. and Dobra, A. (2009). A conjugate prior for discrete hierarchical log-linear models. Ann. Statist. 37 3431–3467.
  Mathematical Reviews (MathSciNet): MR2549565
  Zentralblatt MATH: 1369.62048
  Digital Object Identifier: doi:10.1214/08-AOS669
  Project Euclid: euclid.aos/1250515392
  
• Massey, D. S. (1990). Social structure, household strategies, and the cumulative causation of migration. Popul. Index 56 3–26.
• Massey, D. S. and Espinosa, K. E. (1997). What’s driving Mexico–U.S. migration? A theoretical, empirical, and policy analysis. Am. J. Sociol. 102 939–999.
• Massey, D. S., Arango, J., Hugo, G., Kouaouci, A., Pellegrino, A. and Taylor, J. E. (1993). Theories of international migration: A review and appraisal. Popul. Dev. Rev. 19 431–466.
• Massey, D. S., Williams, N., Axinn, W. G. and Ghimire, D. (2010). Community services and out-migration. Int. Migr. 48 1–41.
• Mohammadi, A. and Dobra, A. (2017). The R package BDgraph for Bayesian structure learning in graphical models. ISBA Bull. 4 11–16.
• Mohammadi, A., Massam, H. and Letac, G. (2017). The ratio of normalizing constants for Bayesian graphical Gaussian model selection. Preprint. Available at arXiv:1706.04416.
  arXiv: 1706.04416
  
• Mohammadi, A. and Wit, E. C. (2015). Bayesian structure learning in sparse Gaussian graphical models. Bayesian Anal. 10 109–138.
  Mathematical Reviews (MathSciNet): MR3420899
  Zentralblatt MATH: 1335.62056
  Digital Object Identifier: doi:10.1214/14-BA889
  Project Euclid: euclid.ba/1422468425
  
• Mohammadi, R. and Wit, E. C. (2017). BDgraph: An R package for Bayesian structure learning in graphical models. Preprint. Available at arXiv:1501.05108v4.
  arXiv: 1501.05108v4
  
• Mohammadi, R. and Wit, E. C. and Dobra, A. (2018). BDgraph: Bayesian structure learning in graphical models using birth–death MCMC. R package version 2.49.
• Mohammadi, A., Abegaz, F., van den Heuvel, E. and Wit, E. C. (2017). Bayesian modelling of Dupuytren disease by using Gaussian copula graphical models. J. R. Stat. Soc. Ser. C. Appl. Stat. 66 629–645.
• Nardi, Y. and Rinaldo, A. (2012). The log-linear group-lasso estimator and its asymptotic properties. Bernoulli 18 945–974.
  Zentralblatt MATH: 1243.62107
  Digital Object Identifier: doi:10.3150/11-BEJ364
  Project Euclid: euclid.bj/1340887009
  
• Neubauer, G., Huber, H., Vogl, A., Jager, B., Preinerstorfer, A., Schirnhofer, S., Schimak, G. and Havlik, D. (2015). On the volume of geo-referenced tweets and their relationship to events relevant for migration tracking. In Environmental Software Systems. Infrastructures, Services and Applications: 11th IFIP WG 5.11 International Symposium, ISESS 2015, Melbourne, VIC, Australia, March 25–27, 2015. Proceedings (R. Denzer, R. M. Argent, G. Schimak and J. Hřebíček, eds.) 520–530. Springer, Cham.
• OpenMP Architecture Review Board (2008). OpenMP application program interface version 3.0.
• Pensar, J., Nyman, H., Niiranen, J. and Corander, J. (2017). Marginal pseudo-likelihood learning of discrete Markov network structures. Bayesian Anal. 12 1195–1215.
  Mathematical Reviews (MathSciNet): MR3724983
  Zentralblatt MATH: 1384.62178
  Digital Object Identifier: doi:10.1214/16-BA1032
  Project Euclid: euclid.ba/1477918728
  
• Preston, C. (1975). Spatial birth-and-death processes. Bull. Inst. Int. Stat. 46 371–391, 405–408 (1975). With discussion.
  Zentralblatt MATH: 0379.60082
  
• Ravikumar, P., Wainwright, M. J. and Lafferty, J. D. (2010). High-dimensional Ising model selection using $\ell_{1}$-regularized logistic regression. Ann. Statist. 38 1287–1319.
  Zentralblatt MATH: 1189.62115
  Digital Object Identifier: doi:10.1214/09-AOS691
  Project Euclid: euclid.aos/1268056617
  
• Raymer, J., Abel, G. and Smith, P. W. F. (2007). Combining census and registration data to estimate detailed elderly migration flows in England and Wales. J. Roy. Statist. Soc. Ser. A 170 891–908.
  Mathematical Reviews (MathSciNet): MR2408983
  Digital Object Identifier: doi:10.1111/j.1467-985X.2007.00490.x
  
• Raymer, J., Wiśniowski, A., Forster, J. J., Smith, P. W. F. and Bijak, J. (2013). Integrated modeling of European migration. J. Amer. Statist. Assoc. 108 801–819.
  Mathematical Reviews (MathSciNet): MR3174664
  Zentralblatt MATH: 06224967
  Digital Object Identifier: doi:10.1080/01621459.2013.789435
  
• Scott, J. G. and Carvalho, C. M. (2008). Feature-inclusion stochastic search for Gaussian graphical models. J. Comput. Graph. Statist. 17 790–808.
• SMaPP (2017). smappR package: Tools for analysis of Twitter data, Social Media and Participation, New York University. Available at https://github.com/SMAPPNYU/smappR.
• Smith, P. W. F., Raymer, J. and Giulietti, C. (2010). Combining available migration data in England to study economic activity flows over time. J. Roy. Statist. Soc. Ser. A 173 733–753.
• Stark, O. and Bloom, D. E. (1985). The new economics of labor migration. Am. Econ. Rev. 75 173–178.
• Stark, O. and Taylor, J. E. (1985). Migration incentives, migration types: The role of relative deprivation. Econ. J. 101 1163–1178.
• Stopher, P. R. and Greaves, S. P. (2007). Household travel surveys: Where are we going? Transp. Res., Part A Policy Pract. 41 367–381.
• Tarantola, C. (2004). MCMC model determination for discrete graphical models. Stat. Model. 4 39–61.
  Zentralblatt MATH: 1111.62025
  Digital Object Identifier: doi:10.1191/1471082X04st063oa
  
• Tatem, A. J. (2014). Mapping population and pathogen movements. Int. Health 6 5–11.
• Taylor, J. E. (1987). Undocumented Mexico–U.S. migration and the returns to households in rural Mexico. Am. J. Agric. Econ. 69 616–638.
• Todaro, M. P. (1969). A model of labor migration and urban unemployment in less developed countries. Am. Econ. Rev. 59 138–148.
• Todaro, M. P. and Maruszko, L. (1987). Illegal immigration and U.S. immigration reform: A conceptual framework. Popul. Dev. Rev. 13 101–114.
• Tsamardinos, I., Brown, L. E. and Aliferis, C. F. (2006). The max–min hill-climbing Bayesian network structure learning algorithm. Mach. Learn. 65 31–78.
• Twitter, Inc. (2017). Twitter REST APIs. Available at https://dev.twitter.com/rest/public.
• VanWey, L. K. (2005). Land ownership as a determinant of international and internal migration in Mexico and internal migration in Thailand. Int. Migr. Rev. 39 141–172.
• Wainwright, M. and Jordan, M. (2008). Graphical models, exponential families and variational inference. Found. Trends Mach. Learn. 1 1–305.
  Zentralblatt MATH: 1193.62107
  Digital Object Identifier: doi:10.1561/2200000001
  
• Wang, H. and Li, S. Z. (2012). Efficient Gaussian graphical model determination under $G$-Wishart prior distributions. Electron. J. Stat. 6 168–198.
  Zentralblatt MATH: 1335.62069
  Digital Object Identifier: doi:10.1214/12-EJS669
  
• Whittaker, J. (1990). Graphical Models in Applied Multivariate Statistics. Wiley, Chichester.
  Mathematical Reviews (MathSciNet): MR1112133
  Zentralblatt MATH: 0732.62056
  
• Williams, N. (2009). Education, gender, and migration in the context of social change. Soc. Sci. Res. 38 883–896.
• Williams, N. E., Thomas, T. A., Dunbar, M., Eagle, N. and Dobra, A. (2015). Measures of human mobility using mobile phone records enhanced with GIS data. PLoS ONE 10 1–16.
• Wolf, J., Oliveira, M. and Thompson, M. (2003). Impact of underreporting on mileage and travel time estimates: Results from global positioning system-enhanced household travel survey. Transp. Res. Rec. 1854 189–198.