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← Reports and unpublished papers

Small Sample Properties of Partially-Observed Rank Data Estimators  

6 October 2011

Christopher Adolph

Several estimators from the social science toolkit might be used to model the relationship between imprecisely-observed ranks in a hierarchy, and covariates explaining those ranks. But application of standard methods – such as linear regression, ordered probit, or censored regression – is complicated by the interdependence of rank observations. Monte Carlo evidence shows that estimators which either delete partially observed ranks and/or inappropriately assume ranks are iid perform poorly, yielding inefficient and sometimes biased estimates, and wildly inaccurate confidence intervals. In contrast, a Bayesian partial rank model – designed to impute missing ranks within known bounds, and account for interdependence across ranks – performs well, even when most or all ranks are observed imprecisely.




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