The Annals of Applied Statistics

A mixture of experts model for rank data with applications in election studies

Isobel Claire Gormley and Thomas Brendan Murphy
Source: Ann. Appl. Stat. Volume 2, Number 4 (2008), 1452-1477.

Abstract

A voting bloc is defined to be a group of voters who have similar voting preferences. The cleavage of the Irish electorate into voting blocs is of interest. Irish elections employ a “single transferable vote” electoral system; under this system voters rank some or all of the electoral candidates in order of preference. These rank votes provide a rich source of preference information from which inferences about the composition of the electorate may be drawn. Additionally, the influence of social factors or covariates on the electorate composition is of interest.

A mixture of experts model is a mixture model in which the model parameters are functions of covariates. A mixture of experts model for rank data is developed to provide a model-based method to cluster Irish voters into voting blocs, to examine the influence of social factors on this clustering and to examine the characteristic preferences of the voting blocs. The Benter model for rank data is employed as the family of component densities within the mixture of experts model; generalized linear model theory is employed to model the influence of covariates on the mixing proportions. Model fitting is achieved via a hybrid of the EM and MM algorithms. An example of the methodology is illustrated by examining an Irish presidential election. The existence of voting blocs in the electorate is established and it is determined that age and government satisfaction levels are important factors in influencing voting in this election.

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Permanent link to this document: http://projecteuclid.org/euclid.aoas/1231424218
Digital Object Identifier: doi:10.1214/08-AOAS178
Zentralblatt MATH identifier: 05505363
Mathematical Reviews number (MathSciNet): MR2655667

References

Airoldi, E. M., Blei, D. M., Fienberg, S. E. and Xing, E. P. (2008). Mixed membership stochastic blockmodels. J. Machine Learning Research 9 1981–2014.
Benter, W. (1994). Computer-based horse race handicapping and wagering systems: A report. In Efficiency of Racetrack Betting Markets (W. T. Ziemba, V. S. Lo and D. B. Haush, eds.) 183–198. Academic Press, San Diego.
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer, New York.
Mathematical Reviews (MathSciNet): MR2247587
Zentralblatt MATH: 1107.68072
Bishop, C. M. and Svensén, M. (2003). Bayesian hierarchical mixture of experts. In Proceedings Nineteenth Conference on Uncertainty in Artificial Intelligence 57–64.
Böhning, D., Dietz, E., Schaub, R., Schlattmann, P. and Lindsay, B. (1994). The distribution of the likelihood ratio for mixtures of densities from the one-parameter exponential family. Ann. Instit. Statist. Math. 46 373–388.
Breiman, L., Friedman, J., Olshen, R. and Stone, C. (2006). Classification and Regression Trees, 3rd ed. Routledge PSAI Press, London.
Zentralblatt MATH: 0541.62042
Busse, L. M., Orbanz, P. and Buhmann, J. M. (2007). Cluster analysis of heterogeneous rank data. In ICML 2007: Proceedings of the 24th International Conference on Machine Learning 113–120. ACM, New York.
Coakley, J. and Gallagher, M. (1999). Politics in the Republic of Ireland, 3rd ed. Routledge PSAI Press, London.
Dempster, A. P., Laird, N. M. and Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm (with discussion). J. Roy. Statist. Soc. Ser. B 39 1–38.
Mathematical Reviews (MathSciNet): MR501537
Dobson, A. J. (2002). An Introduction to Generalized linear Models, 2nd ed. Chapman and Hall, London.
Mathematical Reviews (MathSciNet): MR2068205
Erosheva, E. A., Fienberg, S. E. and Joutard, C. (2007). Describing disability through individual-level mixture models for multivariate binary data. Ann. Appl. Statist. 1 502–537.
Mathematical Reviews (MathSciNet): MR2415745
Zentralblatt MATH: 1126.62101
Digital Object Identifier: doi:10.1214/07-AOAS126
Project Euclid: euclid.aoas/1196438029
Fligner, M. A. and Verducci, J. S. (1986). Distance based ranking models. J. Roy. Statist. Soc. Ser. B 48 359–369.
Mathematical Reviews (MathSciNet): MR876847
Fligner, M. A. and Verducci, J. S. (1988). Multistage ranking models. J. Amer. Statist. Assoc. 83 892–901.
Mathematical Reviews (MathSciNet): MR963820
Zentralblatt MATH: 0719.62036
Digital Object Identifier: doi:10.1080/01621459.1988.10478679
Fraley, C. and Raftery, A. E. (1998). How many clusters? Which clustering methods? Answers via model-based cluster analysis. Computer J. 41 578–588.
Friedman, J. (1991). Multivariate adaptive regression splines. Ann. Statist. 19 1–141.
Mathematical Reviews (MathSciNet): MR1091842
Zentralblatt MATH: 0765.62064
Digital Object Identifier: doi:10.1214/aos/1176347963
Project Euclid: euclid.aos/1176347963
Gelman, A. and Rubin, D. B. (1995). Avoiding model selection in Bayesian social research. Soc. Methodol. 25 165–173.
Gordon, A. D. (1979). A measure of the agreement between rankings. Biometrika 66 7–15.
Mathematical Reviews (MathSciNet): MR529142
Zentralblatt MATH: 0397.62040
Digital Object Identifier: doi:10.1093/biomet/66.1.7
Gormley, I. C. (2006). Statistical models for rank data. Ph.D. thesis, Univ. Dublin, Trinity College.
Gormley, I. C. and Murphy, T. B. (2006). Analysis of Irish third-level college applications data. J. Roy. Statist. Soc. Ser. A 169 361–379.
Mathematical Reviews (MathSciNet): MR2225548
Digital Object Identifier: doi:10.1111/j.1467-985X.2006.00412.x
Gormley, I. C. and Murphy, T. B. (2007). A latent space model for rank data. Statistical Network Analysis: Models, Issues and New Directions. Lecture Notes in Comput. Sci. 4503 90–107. Springer, Berlin.
Gormley, I. C. and Murphy, T. B. (2008a). Exploring voting blocs within the Irish electorate: A mixture modeling approach. J. Amer. Statist. Assoc. 103 1014–1027.
Mathematical Reviews (MathSciNet): MR2528824
Digital Object Identifier: doi:10.1198/016214507000001049
Gormley, I. C. and Murphy, T. B. (2008b). A grade of membership model for rank data. Technical report, Univ. College Dublin. Available at http://www.ucd.ie/statdept/staffpages/cgormley/gom.pdf.
Gormley, I. C. and Murphy, T. B. (2008c). Supplement to “A mixture of experts model for rank data with applications in election studies.” DOI: 10.1214/08-AOAS178SUPP.
Hill, J. L. (2001). Accommodating missing data in mixture models for classification by opinion-changing behavior. J. Educational and Behavioral Statistics 26 233–268.
Holloway, S. (1990). Forty years of united Nations General Assembly voting. Canad. J. Political Sci. / Revue Canad. Sci. Politique 23 279–296.
Hunter, D. R. (2004). MM algorithms for generalized Bradley–Terry models. Ann. Statist. 32 384–406.
Mathematical Reviews (MathSciNet): MR2051012
Zentralblatt MATH: 1105.62359
Digital Object Identifier: doi:10.1214/aos/1079120141
Project Euclid: euclid.aos/1079120141
Hunter, D. R. and Lange, K. (2004). A tutorial on MM algorithms. Amer. Statist. 58 30–37.
Mathematical Reviews (MathSciNet): MR2055509
Digital Object Identifier: doi:10.1198/0003130042836
Jacobs, R. A., Jordan, M. I., Nowlan, S. J. and Hinton, G. E. (1991). Adaptive mixture of local experts. Neural Computation 3 79–87.
Jakulin, A. and Buntine, W. (2004). Analyzing the US Senate in 2003: Similarities, Networks, Clusters and Blocs. Preprint. Available at http://kt.ijs.si/aleks/Politics/us_senate.pdf.
Jordan, M. I. and Jacobs, R. A. (1994). Hierarchical mixture of experts and the EM algorithm. Neural Computation 6 181–214.
Zentralblatt MATH: 1058.68097
Kass, R. E. and Raftery, A. E. (1995). Bayes factors. J. Amer. Statist. Assoc. 90 773–795.
Keribin, C. (1998). Estimation consistante de l’ordre de modèles de mélange. C. R. Acad. Sci. Paris Sér. I Math. 326 243–248.
Mathematical Reviews (MathSciNet): MR1646973
Digital Object Identifier: doi:10.1016/S0764-4442(97)89479-7
Keribin, C. (2000). Consistent estimation of the order of mixture models. Sankhyā Ser. A 62 49–66.
Mathematical Reviews (MathSciNet): MR1769735
Lange, K., Hunter, D. R. and Yang, I. (2000). Optimization transfer using surrogate objective functions (with discussion). J. Comput. Graph. Statist. 9 1–59.
Mathematical Reviews (MathSciNet): MR1819865
Leroux, B. G. (1992). Consistent estimation of a mixing distribution. Ann. Statist. 20 1350–1360.
Mathematical Reviews (MathSciNet): MR1186253
Zentralblatt MATH: 0763.62015
Digital Object Identifier: doi:10.1214/aos/1176348772
Project Euclid: euclid.aos/1176348772
Mallows, C. L. (1957). Nonnull ranking models. Biometrika 44 114–130.
Mathematical Reviews (MathSciNet): MR87267
Zentralblatt MATH: 0087.34001
Marden, J. I. (1995). Analyzing and Modeling Rank Data. Chapman and Hall, London.
Mathematical Reviews (MathSciNet): MR1346107
Zentralblatt MATH: 0853.62006
Marsh, M. (1999). The making of the eight president. In How Ireland Voted 1997 (M. Marsh and P. Mitchell, eds.) 215–242. Westview Press, Boulder, CO.
McCullagh, P. and Nelder, J. A. (1983). Generalized Linear Models. Chapman and Hall, London.
Mathematical Reviews (MathSciNet): MR727836
McFadden, D. G. (1978). Modelling the choice of residential location. Spatial Interaction Theory and Planning Models 75–96.
McLachlan, G. J. and Krishnan, T. (1997). The EM Algorithm and Extensions. Wiley, New York.
Mathematical Reviews (MathSciNet): MR1417721
McLachlan, G. J. and Peel, D. (2000). Finite Mixture Models. Wiley, New York.
Mathematical Reviews (MathSciNet): MR1789474
Meng, X.-L. and Rubin, D. B. (1993). Maximum likelihood estimation via the ECM algorithm: A general framework. Biometrika 80 267–278.
Mathematical Reviews (MathSciNet): MR1243503
Zentralblatt MATH: 0778.62022
Digital Object Identifier: doi:10.1093/biomet/80.2.267
Murphy, T. B. and Martin, D. (2003). Mixtures of distance-based models for ranking data. Comput. Statist. Data Anal. 41 645–655.
Mathematical Reviews (MathSciNet): MR1973732
Peng, F., Jacobs, R. A. and Tanner, M. A. (1996). Bayesian inference in mixtures-of-experts and hierarchical mixtures-of-experts models with application to speech recognition. J. Amer. Statist. Assoc. 91 953–960.
Plackett, R. L. (1975). The analysis of permutations. Appl. Statist. 24 193–202.
Mathematical Reviews (MathSciNet): MR391338
Digital Object Identifier: doi:10.2307/2346567
Pritchard, J. K., Stephens, M. and Peter, D. (2000). Inference of population structure using multilocus genotype data. Genetics 155 945–959.
Zentralblatt MATH: 1083.62537
Raftery, A. E. (1995). Bayesian model selection in social research. Soc. Methodol. 25 111–163.
Schwartz, G. (1978). Estimating the dimension of a model. Ann. Statist. 6 461–464.
Mathematical Reviews (MathSciNet): MR468014
Zentralblatt MATH: 0379.62005
Digital Object Identifier: doi:10.1214/aos/1176344136
Project Euclid: euclid.aos/1176344136
Sinnott, R. (1995). Irish Voters Decide: Voting Behaviour in Elections and Referendums Since 1918. Manchester Univ. Press.
Sinnott, R. (1999). The electoral system, In Politics in the Republic of Ireland (J. Coakley and M. Gallagher, eds.) 99–126. Routledge & PSAI Press, London.
Tam, W. K. (1995). Asians—A monolithic voting bloc? Political Behaviour 17 223–249.
Tanner, M. A. (1996). Tools for Statistical Inference, 3rd ed. Springer, New York.
Mathematical Reviews (MathSciNet): MR1396311
Train, K. E. (2003). Discrete Choice Methods with Simulation. Cambridge Univ. Press.
Mathematical Reviews (MathSciNet): MR2003007
van der Brug, W., van der Eijk, C. and Marsh, M. (2000). Exploring uncharted territory: The Irish presidential election 1997. British J. Political Science 30 631–650.

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