Bootstrap P-values: Why they work so well and how to compute them.
by Professor Chris Lloyd
Abstract: For standard discrete models, bootstrap P-value perform extraordinarily well, much better than asymptotics predict. I trace this to three specific mathematical properties of the bootstrap transformation. First, it corrects boundary anomalies. Second, it improves pivotality of the P-value. Third, it correctly calibrates the P-value. I then move on to how to compute bootstrap (and other more exotic P-values using importance sampling. This involves sampling whole curves from the profile distribution of the P-value. The standard recommendation for choosing the biasing distribution fails for logistic regression. I will identify why it fails and develop a better criterion which works extremely well in all examples considered.
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