A Fast, Easy, and Efficient Estimator for Multiparty Electoral Data
Abstract
Katz and King have previously developed a model for predicting or explaining aggregate electoral results in multiparty democracies. Their model is, in principle, analogous to what least-squares regression provides American political researchers in that two-party system. Katz and King applied their model to three-party elections in England and revealed a variety of new features of incumbency advantage and sources of party support. Although the mathematics of their statistical model covers any number of political parties, it is computationally demanding, and hence slow and numerically imprecise, with more than three parties. In this paper we produce an approximate method that works in practice with many parties without making too many theoretical compromises. Our approach is to treat the problem as one of missing data. This allows us to use a modification of the fast EMis algorithm of King, Honaker, Joseph, and Scheve and to provide easy-to-use software, while retaining the attractive features of the Katz and King model, such as thetdistribution and explicit models for uncontested seats.
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Compositional data analysis is a mature and well-established field of statistics.
“Compositional data analysis is currently a mature and well-established field of statistics which has been used in diverse fields of research concerned with multivariate proportion-type data such as nutrition (e.g. fatty acid composition in meat) [19], geochemistry (e.g. sedimentary composition of rocks or chemistry of groundwaters) [20, 21], politics (e.g. multiparty electoral data) [22], and”
Cites
Maximum Likelihood from Incomplete Data Via the <i>EM</i> Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444 · see citing works
Statistical Analysis With Missing Data
Lahiff, M., Little, R. J. A., & Rubin, D. B. (1989) · Journal of the American Statistical Association · doi:10.2307/2289883 · Cited by 17,384 · see citing works
Analysis of Incomplete Multivariate Data
Schafer, J. L. (1997) · doi:10.1201/9781439821862 · Cited by 6,012 · see citing works
Analysis of Incomplete Multivariate Data
Booth, D. E., & Schafer, J. L. (2000) · Technometrics · doi:10.2307/1271466 · Cited by 5,627 · see citing works
The Statistical Analysis of Compositional Data
Aitchison, J. (1986) · doi:10.1007/978-94-009-4109-0 · Cited by 4,383 · see citing works
The Statistical Analysis of Compositional Data
Piepel, G. F., & Aitchison, J. (1988) · Technometrics · doi:10.2307/1270335 · Cited by 3,410 · see citing works
Making the most of statistical analyses: improving interpretation and presentation
King, G., Tomz, M., & Wittenberg, J. (2000) · American Journal of Political Science · doi:10.2307/2669316 · Cited by 3,232 · see citing works
Self-citationAnalyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation
King, G., Honaker, J., Joseph, A., & Scheve, K. (2001) · American Political Science Review · doi:10.1017/s0003055401000235 · Cited by 1,834 · see citing works
Self-citationThe ECME Algorithm: A Simple Extension of EM and ECM with Faster Monotone Convergence
Liu, C., & Rubin, D. B. (1994) · Biometrika · doi:10.1093/biomet/81.4.633 · Cited by 607 · see citing works
Missing Values in Multivariate Analysis
Beale, E. M. L., & Little, R. J. A. (1975) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1975.tb01037.x · Cited by 349 · see citing works
A Statistical Model for Multiparty Electoral Data
Katz, J. N., & King, G. (1999) · American Political Science Review · doi:10.2307/2585758 · Cited by 297 · see citing works
Self-citationA Unified Method of Evaluating Electoral Systems and Redistricting Plans
Gelman, A., & King, G. (1994) · American Journal of Political Science · doi:10.2307/2111417 · Cited by 229 · see citing works
Self-citationGibson, J. G., & Cielecka, A. (1995) · Europe Asia Studies · doi:10.1080/09668139508412287 · Cited by 54 · see citing works
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Chastin, S. F. M., Palarea-Albaladejo, J., Dontje, M. L., & Skelton, D. A. (2015) · PLOS ONE · doi:10.1371/journal.pone.0139984 · Cited by 1,034 · see citing works
- Authors
- Honaker, J., Katz, J. N., & King, G.
- Affiliations
- University of California, Los Angeles (US)
- California Institute of Technology (US)
- Harvard University (US)
- Venue
- Political Analysis
- Publisher
- Cambridge University Press (CUP)
- Published
- 2002
- Volume
- 10
- Issue
- 1
- Pages
- 84-100
- ISSN
- 1047-1987 (ISSN-L)
- 1476-4989
- OpenAlex
- W2104603709
- Cited by
- 5
- References
- 13
- Citations by year
- 2020: 1 · 2018: 1 · 2015: 1 · 2013: 1
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Political Analysis (publishedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Political Science and International Relations › Electoral Systems and Political Participation
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Political Science and International Relations › Political Science Research and Education
- Keywords
- multiparty elections, missing data, incumbency advantage, party support, t-distribution, computational efficiency
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