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Introduction to the Special Issue

Published online by Cambridge University Press:  04 January 2017

Jeff Gill
Jeff Gill*
Affiliation:
Department of Political Science, University of California, Davis, One Shields Avenue, Davis, CA 95616. e-mail: jgill@ucdavis.edu
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Extract

Welcome to the special issue of Political Analysis dedicated to Bayesian methods. We hope that you enjoy the varied and interesting contributions herein featuring Bayesian statistical methods. For many people in empirical political science, Bayesian statistics seems like a weird offshoot of probability that surfaces occasionally in journals and books but does not occupy a particularly central role. This perception appears to be changing. In fact, it appears to be changing quite rapidly. The purpose of this issue is to support and accelerate this momentum by further demonstrating the full flexibility and power of Bayesian methodology.

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Type
Research Article
Information
Political Analysis , Volume 12 , Issue 4: Special Issue on Bayesian Methods , Autumn 2004, pp. 323 - 337
Copyright
Copyright © Society for Political Methodology 2004 

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References

Bartels, Larry M. 1996. “Pooling Disparate Observations.” American Journal of Political Science 40: 905–942.CrossRefGoogle Scholar
Bartels, Larry M. 1997. “Specification Uncertainty and Model Averaging.” American Journal of Political Science 41: 641–674.CrossRefGoogle Scholar
Berger, J. O. 1990. “Robust Analysis: Sensitivity to the Prior.” Journal of Statistical Planning and Inference 25: 303–328.CrossRefGoogle Scholar
Casella, G., and Berger, R. L. 2001. Statistical Inference, 2nd ed. Belmont, CA: Duxbury.Google Scholar
Clarke, Kevin A. 2001. “Testing Nonnested Models of International Relations: Reevaluating Realism.” American Journal of Political Science 45: 724–744.CrossRefGoogle Scholar
Cornfield, Jerome. 1969. “The Bayesian Outlook and Its Application.” Biometrics 25: 617–657.CrossRefGoogle ScholarPubMed
de Finetti, B. 1972. Probability, Induction, and Statistics. New York: John Wiley & Sons.Google Scholar
de Finetti, B. 1974. Theory of Probability, Volume 1. New York: John Wiley & Sons.Google Scholar
de Finetti, B. 1975. Theory of Probability, Volume 2. New York: John Wiley & Sons.Google Scholar
Diaconis, P., and Freedman, D. A. 1986. “On the Consistency of Bayes Estimates.” Annals of Statistics 14: 1–67.Google Scholar
Draper, David. 1995. “Asessment and Propagation of Model Uncertainty.” Journal of the Royal Statistical Society, Series B 57: 45–97.Google Scholar
Ehrlich, I. 1975. “The Deterrent Effect of Capital Punishment: A Question of Life or Death.” American Economic Review 65: 397–417.Google Scholar
Ehrlich, I. 1977. “Capital Punishment and Deterrence: Some Further Thoughts and Additional Evidence.” Journal of Political Economy 85: 741–788.CrossRefGoogle Scholar
Fisher, R. A. 1935. “The Fiducial Argument in Statistical Inference.” Annals of Eugenics 6: 391–398.CrossRefGoogle Scholar
Freedman, D. A. 1963. “On the Asymptotic Behavior of Bayes’ Estimates in the Discrete Case.” Annals of Mathematical Statistics 34: 1386–1403.CrossRefGoogle Scholar
Freedman, D. A. 1965. “On the Asymptotic Behavior of Bayes’ Estimates in the Discrete Case II.” Annals of Mathematical Statistics 36: 454–456.CrossRefGoogle Scholar
Gelfand, A. E., and Smith, A. F. M. 1990. “Sampling-Based Approaches to Calculating Marginal Densities.” Journal of the American Statistical Association 85: 389–409.CrossRefGoogle Scholar
Gelman, Andrew, and King, Gary. 1994. “A Unified Method of Evaluating Electoral Systems and Redistricting Plans.” American Journal of Political Science 38: 514–554.CrossRefGoogle Scholar
Gelman, A., and Rubin, D. B. 1992. “Inference from Iterative Simulation Using Multiple Sequences.” Statistical Science 7: 457–511.CrossRefGoogle Scholar
Geman, S., and Geman, D. 1984. “Stochastic Relaxation, Gibbs Distributions and the Bayesian Restoration of Images.” IEEE Transactions on Pattern Analysis and Machine Intelligence 6: 721–741.CrossRefGoogle ScholarPubMed
Geweke, J. 1992. “Evaluating the Accuracy of Sampling-Based Approaches to the Calculation of Posterior Moments.” In Bayesian Statistics 4, eds. Bernardo, J. M., Smith, A. F. M., Dawid, A. P., and Berger, J. O. Oxford: Oxford University Press, pp. 169–193.CrossRefGoogle Scholar
Geyer, C. J. 1992. “Practical Markov Chain Monte Carlo.” Statistical Science 7: 473–511.Google Scholar
Gill, Jeff. 1999. “The Insignificance of Null Hypothesis Significance Testing.” Political Research Quarterly 52: 647–674.CrossRefGoogle Scholar
Gill, Jeff. 2001. “Whose Variance Is It Anyway? Interpreting Empirical Models with State-Level Data.” State Politics and Policy Quarterly 1: 313–338.CrossRefGoogle Scholar
Gill, Jeff. 2002. Bayesian Methods: A Social and Behavioral Sciences Approach. New York: Chapman and Hall/CRC.CrossRefGoogle Scholar
Good, I. J. 1950. Probability and the Weighting of Evidence. London: Griffin.Google Scholar
Hastings, W. K. 1970. “Monte Carlo Sampling Methods Using Markov Chains and Their Applications.” Biometrika 57: 97–109.CrossRefGoogle Scholar
Hill, Jennifer L., and Kriesi, Hanspeter. 2001. “Classification by Opinion-Changing Behavior: A Mixture Model Approach.” Political Analysis 9: 301–324.CrossRefGoogle Scholar
Hoeting, Jennifer A., Madigan, David, Raftery, Adrian E., and Volinsky, Chris T. 1999. “Bayesian Model Averaging: A Tutorial.” Statistical Science 14: 382–401.Google Scholar
Jackman, Simon. 2000a. “Estimation and Inference Are Missing Data Problems: Unifying Social Science Statistics via Bayesian Simulation.” Political Analysis 8: 307–332.CrossRefGoogle Scholar
Jackman, Simon. 2000b. “Estimation and Inference via Bayesian Simulation: An Introduction to Markov Chain Monte Carlo.” American Journal of Political Science 44: 375–404.CrossRefGoogle Scholar
Jackman, Simon. 2001. “Multidimensional Analysis of Roll Call Data via Bayesian Simulation: Identification, Estimation, Inference, and Model Checking.” Political Analysis 9: 227–241.CrossRefGoogle Scholar
Jeffreys, H. 1961. Theory of Probability. Oxford, UK: Oxford University Press.Google Scholar
Leamer, Edward E. 1978. Specification Searches: Ad Hoc Inference with Nonexperimental Data. New York: John Wiley & Sons.Google Scholar
Leamer, Edward E. 1983. “Let's Take the Con Out of Econometrics.” American Economic Review 73: 31–43.Google Scholar
Lindley, D. V. 1958. “Fiducial Distributions and Bayes’ Theory.” Journal of the Royal Statistical Society, Series B 20: 102–107.Google Scholar
Lindley, D. V. 1961. “The Use of Prior Probability Distributions in Statistical Inference and Decision.” In Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability. Berkeley: University of California Press, pp. 453–468.Google Scholar
Lindley, D. V. 1965. Introduction to Probability and Statistics from a Bayesian Viewpoint, Parts 1 and 2. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Martin, Andrew, and Quinn, Kevin. 2002. “Dynamic Ideal Point Estimation via Markov Chain Monte Carlo for the U.S. Supreme Court, 1953–1999.” Political Analysis 10: 134–153.CrossRefGoogle Scholar
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and Teller, E. 1953. “Equations of State Calculations by Fast Computing Machine.” Journal of Chemical Physics 21: 1087–1091.CrossRefGoogle Scholar
Quinn, Kevin M., Martin, Andrew D., and Whitford, Andrew B. 1999. “Voter Choice in Multi-party Democracies: A Test of Competing Theories and Models.” American Journal of Political Science 43: 1231–1247.CrossRefGoogle Scholar
Raftery, Adrian E. 1995. “Bayesian Model Selection in Social Research.” Sociological Methodology 25: 111–163.CrossRefGoogle Scholar
Raftery, Adrian E., Madigan, David, and Hoeting, Jennifer A. 1997. “Bayesian Model Averaging for Linear Regression Models.” Journal of the American Statistical Association 92: 179–191.CrossRefGoogle Scholar
Robert, C. P., and Casella, G. 2004. Monte Carlo Statistical Methods, 2nd ed. New York: Springer-Verlag.CrossRefGoogle Scholar
Savage, L. J. 1954. The Foundations of Statistics. New York: Wiley.Google Scholar
Savage, L. J. 1962. The Foundations of Statistical Inference. London: Methuen.Google Scholar
Smith, Alastair. 1998. “A Summary of Political Selection: The Effect of Strategic Choice on the Escalation of International Crises.” American Journal of Political Science 42: 698–701.CrossRefGoogle Scholar
Smith, Alastair. 1999. “Testing Theories of Strategic Choice: The Example of Crisis Escalation.” American Journal of Political Science 43: 1254–1283.CrossRefGoogle Scholar
Spiegelhalter, David J., Best, Nicola G., Carlin, Bradley P., and van der Linde, Angelika. 2002. “Bayesian Measures of Model Complexity and Fit.” Journal of the Royal Statistical Society, Series B 64: 583–639.CrossRefGoogle Scholar
Western, B. 1998. “Causal Heterogeneity in Comparative Research: A Bayesian Hierarchical Modelling Approach.” American Journal of Political Science 42: 1233–1259.CrossRefGoogle Scholar
Western, Bruce, and Jackman, Simon. 1994. “Bayesian Inference for Comparative Research.” American Political Science Review 88: 412–423.CrossRefGoogle Scholar