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Part 1: Document Description
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Citation |
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Title: |
Replication data for: Estimation and Inference Are Missing Data Problems: Unifying Social Science Statistics via Bayesian Simulation |
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Identification Number: |
doi:10.7910/DVN/SEAXTK |
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Distributor: |
Harvard Dataverse |
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Date of Distribution: |
2010-02-16 |
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Version: |
1 |
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Bibliographic Citation: |
Simon Jackman, 2010, "Replication data for: Estimation and Inference Are Missing Data Problems: Unifying Social Science Statistics via Bayesian Simulation", https://doi.org/10.7910/DVN/SEAXTK, Harvard Dataverse, V1 |
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Citation |
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Title: |
Replication data for: Estimation and Inference Are Missing Data Problems: Unifying Social Science Statistics via Bayesian Simulation |
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Identification Number: |
doi:10.7910/DVN/SEAXTK |
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Authoring Entity: |
Simon Jackman (Department of Political Science, Stanford University, Stanford) |
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Producer: |
Political Analysis |
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Date of Production: |
2000 |
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Distributor: |
Harvard Dataverse |
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Distributor: |
Murray Research Archive |
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Date of Deposit: |
2009 |
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Holdings Information: |
https://doi.org/10.7910/DVN/SEAXTK |
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Study Scope |
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Abstract: |
Bayesian simulation is increasingly exploited in the social sciences for estimation and inference of model parameters. But an especially useful (if often overlooked) feature of Bayesian simulation is that it can be used to estimate any function of model parameters, including “auxiliary” quantities such as goodness-of-fit statistics, predicted values, and residuals. Bayesian simulation treats these quantities as if they were missing data, sampling from their implied posterior densities. Exploiting this principle also lets researchers estimate models via Bayesian simulation where maximum-likelihood estimation would be intractable. Bayesian simulation thus provides a unified solution for quantitative social science. I elaborate these ideas in a variety of contexts: these include generalized linear models for binary responses using data on bill cosponsorship recently reanalyzed in Political Analysis, item–response models for the measurement of respondent’s levels of political information in public opinion surveys, the estimation and analysis of legislators’ ideal points from roll-call data, and outlier-resistant regression estimates of incumbency advantage in U.S. Congressional elections. |
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Data Access |
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Notes: |
<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a> |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Title: |
Simon Jackman. 2000. "Estimation and Inference Are Missing Data Problems: Unifying Social Science Statistics via Bayesian Simulation." Political Analysis, 8(4), 307 - 332. <a href= "http://polmeth.wustl.edu/polanalysis/vol/8/PA84-307-332.pdf" target= "_new">article available here</a> |
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Bibliographic Citation: |
Simon Jackman. 2000. "Estimation and Inference Are Missing Data Problems: Unifying Social Science Statistics via Bayesian Simulation." Political Analysis, 8(4), 307 - 332. <a href= "http://polmeth.wustl.edu/polanalysis/vol/8/PA84-307-332.pdf" target= "_new">article available here</a> |
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Label: |
EstimationInferenceMissingData.pdf |
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Text: |
Published Article |
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Notes: |
application/pdf |
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Label: |
jackman84.zip |
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Text: |
The associated materials by the author |
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Notes: |
application/zip |