Statistics – Computation
Scientific paper
2010-04-16
Statistics
Computation
26 pages, 3 figures, 3 tables [significant rewrite of version 1, including additional examples, title change]
Scientific paper
Importance sampling is a common technique for Monte Carlo approximation, including Monte Carlo approximation of p-values. Here it is shown that a simple correction of the usual importance sampling p-values creates valid p-values, meaning that a hypothesis test created by rejecting the null when the p-value is <= alpha will also have a type I error rate <= alpha. This correction uses the importance weight of the original observation, which gives valuable diagnostic information under the null hypothesis. Using the corrected p-values can be crucial for multiple testing and also in problems where evaluating the accuracy of importance sampling approximations is difficult. Inverting the corrected p-values provides a useful way to create Monte Carlo confidence intervals that maintain the nominal significance level and use only a single Monte Carlo sample. Several applications are described, including accelerated multiple testing for a large neurophysiological dataset and exact conditional inference for a logistic regression model with nuisance parameters.
No associations
LandOfFree
Conservative Hypothesis Tests and Confidence Intervals using Importance Sampling does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Conservative Hypothesis Tests and Confidence Intervals using Importance Sampling, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Conservative Hypothesis Tests and Confidence Intervals using Importance Sampling will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-624542