Bayesian metric multidimensional scaling

Political Analysis

Volumn 21, Issue 1, 2013, Pages 125-140

  Bakker, Ryan ^a   Poole, Keith T ^a  

^a UNIVERSITY OF GEORGIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84872374926     PISSN: 10471987     EISSN: 14764989     Source Type: Journal    
DOI: 10.1093/pan/mps039     Document Type: Article

References (44)

1. Abrajano, Marisa, and Keith T. Poole. 2011. A method of linking surveys using affective signature with an application to ethnic groups in the U.S. In Who Gets Represented, eds. Peter Enns and Christopher Wlezien. New York: Russell Sage.
2. Adams, James, Samuel Merrill, and Bernard Grofman. 2005. A unified theory of party competition: A cross-national analysis integrating spatial and behavioral factors. New York: Cambridge University Press.
3. Bakker, Ryan, and Keith T. Poole. 2012. Replication data for: Bayesian Metric Multidimensional Scaling. http://hdl.handle.net/1902.1/18965 IQSS Dataverse Network [Distributor] V1 [Version]. Also available at http://voteview.com/Bakker_Poole_Bayesian_MDS.htm.
4. Bradlow, Eric T., and David C. Schmittlein. 2000. The little engines that could: Modeling the performance of World Wide Web search engines. Marketing Science 19(1):43-62.
5. Brady, Henry. 1990. Traits versus issues: Factor versus ideal-point analysis of candidate thermometer ratings. Political Analysis 2:97-129.
6. Cahoon, Lawrence S., Melvin J. Hinich, and Peter C. Ordeshook. 1978. A statistical multidimensional scaling method based on the spatial theory of voting. In Graphical Representation of Multivariate Data, ed. P.C. Wang. New York: Academic Press.
7. Clinton, Joshua D., Simon D. Jackman, and Douglas Rivers. 2004. The statistical analysis of roll call data: A unified approach. American Political Science Review 98:355-70.
8. De Leeuw, Jan. 1977. Applications of convex analysis to multidimensional scaling. In Recent Developments in Statistics, eds. J.R. Barra, F. Brodeau, G. Romier, and B. van Cutsem, 133-45. Amsterdam, The Netherlands: North-Holland.
9. De Leeuw, Jan. 1988. Convergence of the majorization method for multidimensional scaling. Journal of Classification 5:163-80.
10. De Leeuw, Jan, and Heiser Willem. 1977. Convergence of correction-matrix algorithms for multidimensional scaling. In Geometric Representations of Relational Data, eds. J.C. Lingoes, E.E. Roskam, and I. Borg, 735-52. Ann Arbor, MI: Mathesis Press.
11. de Leeuw, Jan, and P. Mair. 2009. Multidimensional scaling using majorization: The R package smacof. Journal of Statistical Software 31(3):1-30, http://www.jstatsoft.org/v31/i03/.
12. DeSarbo, Wayne S., Joonwook Park, and Vithala R. Rao. 2011. Deriving joint space positioning maps from consumer preference ratings. Marketing Letters 22(1):1-14.
13. Eckart, Carl H., and Gale Young. 1936. The approximation of one matrix by another of lower rank. Psychometrika 1:211-8.
14. Enelow, James M., and Melvin J. Hinich. 1984. The Spatial Theory of Voting. New York: Cambridge University Press.
15. Fong, Duncan K. H., Wayne S. DeSarbo, Joonwook Park, and Crystal J. Scott. 2010. A Bayesian vector multidimensional scaling procedure for the analysis of ordered preference data. Journal of the American Statistical Association 105(490):482-92.
16. Gormley, Isobel C., and Thomas B. Murphy. 2006. A latent space model for rank data. Statistical Network Analysis: Models, Issues, and New Directions: Lecture Notes in Computer Science. New York: Springer.
17. Hinich, Melvin J. 2005. A new method for statistical multidimensional unfolding. Communications in Statistics-Theory and Methods 34:2299-310.
18. Hoff, Peter D., Adrian E. Raftery, and Mark S. Handcock. 2002. Latent space approaches to social network analysis. Journal of the American Statistical Association 97(460):1090-98.
19. Jacoby, William G. 1982. Unfolding the party identification scale: Improving the measurement an important concept. Political Methodology 8:33-60.
20. Kruskal, Joseph B. 1964a. Multidimensional scaling by optimizing a goodness of fit to a nonmetric hypothesis. Psychometrika 29:1-27.
21. Kruskal, Joseph B. 1964b. Nonmetric multidimensional scaling: A numerical method. Psychometrika 29:115-29.
22. Liu, Dong C., and Jorge Nocedal. 1989. On the limited memory BFGS method. Mathematical Programming 45(1-3):503-28.
23. Londregan, John B. 2000. Estimating legislators' preferred points. Political Analysis 8(1):35-56.
24. Martin, Andrew D., and Kevin M. Quinn. 2002. Dynamic ideal point estimation via Markov chain Monte Carlo for the U.S. Supreme Court, 1953-1999. Political Analysis 10:134-53.
25. Merrill, Samuel, III, and Bernard Grofman. 1999. A unified theory of voting: Directional and proximity spatial models. New York: Cambridge University Press.
26. Navarro, Daniel J., and Michael D. Lee. 2003. Combining dimensions and features in similarity-based representations. In Advances in Neural Information Processing Systems, eds. S. Becker, S. Thrun, and K. Obermayer, Vol. 15, 59-66. Cambridge, MA: MIT Press.
27. Neal, Radford M. 2003. Slice sampling. Annals of Statistics 31(3):705-67.
28. Nelder, John A., and Roger Mead. 1965. A simplex method for function minimization. Computer Journal 7:308-13.
29. Oh, Man-Suk, and Adrian E. Raftery. 2001. Bayesian multidimensional scaling and choice of dimension. Journal of the American Statistical Association 96(455):1031-44.
30. Okada, Kensuke, and Kazuo Shigemasu. 2010. Bayesian multidimensional scaling for the estimation of a Minkowski exponent. Behavior Research Methods 42(4):899-905.
31. Park, Joonwook, Wayne S. DeSarbo, and John Liechty. 2008. A hierarchical Bayesian multidimensional scaling methodology for accommodating both structural and preference heterogeneity. Psychometrika 73(3):451-72.
32. Poole, Keith T., and Howard Rosenthal. 1984. U.S. presidential elections 1968-1980: A spatial analysis. American Journal of Political Science 28:282-312.
33. Poole, Keith T., and Howard Rosenthal. 1997. Congress: A political-economic history of roll call voting. New York: Oxford University Press.
34. Pope, Jeremy C., and Shawn A. Treier. 2011. Reconsidering the great compromise at the federal convention of 1787: Deliberation and agenda effects on the senate and slavery. American Journal of Political Science 55:289-306.
35. Powell, Michael J. D. 1973. On search directions for minimization algorithms. Mathematical Programming 4:193-201.
36. Rabinowitz, George. 1976. A procedure for ordering object pairs consistent with the multidimensional unfolding model. Psychometrika 45:349-73.
37. Schönemann, Peter H. 1970. On metric multidimensional unfolding. Psychometrika 35:349-66.
38. Shepard, Roger N. 1962a. The analysis of proximities: Multidimensional scaling with an unknown distance function. I. Psychometrika 27:125-39.
39. Shepard, Roger N. 1962b. The analysis of proximities: Multidimensional scaling with an unknown distance function. II. Psychometrika 27:219-46.
40. Torgerson, Warren S. 1952. Multidimensional scaling: I. Theory and method. Psychometrika 17:401-19.
41. Torgerson, Warren S. 1958. Theory and methods of scaling. New York: Wiley.
42. Wang, Ming-Mei, Peter H. Schonemann, and Jerrold G. Rusk. 1975. A conjugate gradient algorithm for the multidimensional analysis of preference data. Multivariate Behavioral Research 10:45-80.
43. Weisberg, Herbert F., and Jerrold G. Rusk. 1970. Dimensions of candidate evaluation. American Political Science Review 64:1167-85.
44. Young, Gale, and Alston S. Householder. 1938. Discussion of a set of points in terms of their mutual distances. Psychometrika 3:19-22.