References
- Aslam, J. A., and Montague, M. (2001), “Models for Metasearch,” in Proceedings of the 24th annual international ACM SIGIR Conference on Research and Development in Information Retrieval, New York: ACM, pp. 276–284.
- Bader, M. (2011), “The Transposition Median Problem is NP-Complete,” Theoretical Computer Science, 412, 1099–1110. DOI: 10.1016/j.tcs.2010.12.009.
- Bhowmik, A., and Ghosh, J. (2017), “LETOR Methods for Unsupervised Rank Aggregation,” in Proceedings of the 26th International Conference on World Wide Web, International World Wide Web Conferences Steering Committee, pp. 1331–1340.
- Borda, J. C. (1781), “Mémoire sur les Élections au Scrutin,” Histoire del’ Académie Royale des Sciences.
- Chen, J., Long, R., Wang, X., Liu, B., and Chou, K. (2016), “dRHP-PseRA: Detecting Remote Homology Proteins Using Profile-Based Pseudo Protein Sequence and Rank Aggregation,” Scientific Reports, 6. DOI: 10.1038/srep32333.
- Chen, Y., Fan, J., Ma, C., and Wang, K. (2019), “Spectral Method and Regularized MLE are Both Optimal for Top-K Ranking,” Annals of Statistics, 47, 2204.
- Chen, Y., and Suh, C. (2015), “Spectral MLE: Top-K Rank Aggregation From Pairwise Comparisons,” in International Conference on Machine Learning, 371–380.
- Deconde, R. P., Hawley, S., Falcon, S., Clegg, N., Knudsen, B., and Etzioni, R. (2011), “Combining Results of Microarray Experiments: a Rank Aggregation Approach,” Statistical Applications in Genetics & Molecular Biology, 5, 1544–6115.
- Deng, K., Han, S., Li, K. J., and Liu, J. S. (2014), “Bayesian Aggregation of Order-Based Rank Data,” Journal of the American Statistical Association, 109, 1023–1039. DOI: 10.1080/01621459.2013.878660.
- Diaconis, P. (1988), “Group Representations in Probability and Statistics,” Lecture Notes-Monograph Series, 11, 1–192.
- Diaconis, P., and Graham, R. L. (1977), “Spearman’s Footrule as a Measure of Disarray,” Journal of the Royal Statistical Society, Series B, 39, 262–268. DOI: 10.1111/j.2517-6161.1977.tb01624.x.
- Dwork, C., Kumar, R., Naor, M., and Sivakumar, D. (2001), “Rank Aggregation Methods for the Web,” in Proceedings of the 10th International Conference on World Wide Web, Hong Kong: ACM, pp. 613–622. DOI: 10.1145/371920.372165.
- Fagin, R., Kumar, R., and Sivakumar, D. (2003), “Efficient Similarity Search and Classification Via Rank Aggregation,” in Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, San Diego, CA: ACM, pp. 301–312. DOI: 10.1145/872757.872795.
- Fligner, M. A., and Verducci, J. S. (1986), “Distance Based Ranking Models,” Journal of the Royal Statistical Society, Series B, 48, 359–369. DOI: 10.1111/j.2517-6161.1986.tb01420.x.
- Freund, Y., Iyer, R., Schapire, R. E., and Singer, Y. (2003), “An Efficient Boosting Algorithm for Combining Preferences,” Journal of Machine Learning Research, 4, 933–969.
- Hastings, W. K. (1970), “Monte Carlo Sampling Methods Using Markov Chains and Their Applications,” Biometrika, 57, 97–109. DOI: 10.1093/biomet/57.1.97.
- Irurozki, E., Calvo, B., and Lozano, J. A. (2014), “Permallows: An R Package for Mallows and Generalized Mallows Models,” Journal of Statistical Software, 71, 1–30.
- Johnson, S. R., Henderson, D. A., and Boys, R. J. (2020), “Revealing Subgroup Structure in Ranked Data Using a Bayesian WAND,” Journal of the American Statistical Association, 115, 1888–1901. DOI: 10.1080/01621459.2019.1665528.
- Li, H., Xu, M., Liu, J. S., and Fan, X. (2020), “An Extended Mallows Model for Ranked Data Aggregation,” Journal of the American Statistical Association, 115, 730–746. DOI: 10.1080/01621459.2019.1573733.
- Li, X., Yi, D., and Liu, J. S. (2021), “Bayesian Analysis of Rank Data With Covariates and Heterogeneous Rankers,” Statistical Science.
- Lin, S. (2010), “Space Oriented Rank-Based Data Integration,” Statistical Applications in Genetics & Molecular Biology, 9, article 20.
- Lin, S., and Ding, J. (2010), “Integration of Ranked Lists Via Cross Entropy Monte Carlo With Applications to mRNA and microRNA Studies,” Biometrics, 65, 9–18. DOI: 10.1111/j.1541-0420.2008.01044.x.
- Linas, B., Tadas, M., and Francesco, R. (2010), “Group Recommendations With Rank Aggregation and Collaborative Filtering,” in Proceedings of the Fourth ACM Conference on Recommender Systems, Barcelona, Spain: ACM, pp. 119–126.
- Liu, J. S. (2008), Monte Carlo Strategies in Scientific Computing. Springer Science & Business Media, New York: Springer-Verlag.
- Liu, Y., Liu, T., Qin, T., Ma, Z., and Li, H. (2007), “Supervised Rank Aggregation,” in Proceedings of the 16th International Conference on World Wide Web, Banff Alberta, Canada: ACM, pp. 481–490. DOI: 10.1145/1242572.1242638.
- Luce, R. D. (1959), Individual Choice Behavior: A Theoretical Analysis, New York: Wiley.
- Mallows, C. L. (1957), “Non-Null Ranking Models,” Biometrika, 44, 114–130. DOI: 10.1093/biomet/44.1-2.114.
- Plackett, R. L. (1975), “The Analysis of Permutations,” Journal of the Royal Statistical Society, Series C, 24, 193–202. DOI: 10.2307/2346567.
- Porello, D., and Endriss, U. (2012), “Ontology Merging as Social Choice: Judgment Aggregation Under the Open World Assumption,” Journal of Logic and Computation, 24, 1229–1249. DOI: 10.1093/logcom/exs056.
- Rajkumar, A., and Agarwal, S. (2014), “A Statistical Convergence Perspective of Algorithms for Rank Aggregation From Pairwise Data,” in International Conference on Machine Learning, Beijing, China, pp. 118–126.
- Renda, M. E., and Straccia, U. (2003), “Web Metasearch: Rank vs. Score Based Rank Aggregation Methods,” in Proceedings of the 2003 ACM Symposium on Applied Computing, Melbourne, FL: ACM, pp. 841–846.
- Soufiani, H. A., Parkes, D. C., and Xia, L. (2014), “A Statistical Decision-Theoretic Framework for Social Choice,” in Advances in Neural Information Processing Systems, eds. Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger, Montreal, Quebec, Canada: Curran Associates, Inc., pp. 3185–3193.
- Tanner, M. A., and Wong, W. H. (1987), “The Calculation of Posterior Distributions by Data Augmentation,” Journal of the American Statistical Association, 82, 528–540. DOI: 10.1080/01621459.1987.10478458.
- Thurstone, L. L. (1927), “A Law of Comparative Judgment,” Psychological Review, 34, 273–286. DOI: 10.1037/h0070288.
- Wei, G. C. G., and Tanner, M. A. (1990), “A Monte Carlo Implementation of the EM Algorithm and the Poor Man’s Data Augmentation Algorithms,” Journal of the American Statistical Association, 85, 699– 704. DOI: 10.1080/01621459.1990.10474930.
- Yang, K. H. (2018), Chapter 7—“Stepping Through Finite Element Analysis,” in Basic Finite Element Method as Applied to Injury Biomechanics, ed. K.-H. Yang, Cambridge, MA: Academic Press, pp. 281–308.
- Young, H. P. (1988), “Condorcet’s Theory of Voting,” American Political Science Review, 82, 1231–1244. DOI: 10.2307/1961757.
- Young, H. P., and Levenglick, A. (1978), “A Consistent Extension of Condorcet’s Election Principle,” SIAM Journal on Applied Mathematics, 35, 285–300. DOI: 10.1137/0135023.
- Yu, P. L. H. (2000). “Bayesian Analysis of Order-Statistics Models for Ranking Data,” Psychometrika, 65, 281–299. DOI: 10.1007/BF02296147.