Consistent Confidence Intervals for Maximum Pseudolikelihood Estimators
نویسندگان
چکیده
Maximum pseudolikelihood estimation (MPLE) constitutes a computationally efficient and easily implemented alternative to maximum likelihood and simulationbased methods. The MPLE has been shown to be consistent and asymptotically normally distributed in a number of interesting cases. However, the coverage probability of the conventional confidence interval for the MPLE is biased downward. We provide a bootstrap method for consistently estimating confidence intervals for the MPLE. We then apply this method to the U.S. Supreme Court, where Justices’ votes on cases are characterized as a fully visible Boltzmann machine.
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