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Title: Efficient Monte Carlo evaluation of resampling-based hypothesis tests Authors:  Wing Kam Fung - University of Hong Kong (Hong Kong) [presenting]
Abstract: Monte Carlo evaluation of resampling-based tests is often conducted in statistical analysis. However, this procedure is generally computationally intensive. The pooling resampling-based method has been developed to reduce the computational burden but the validity of the method has not been studied before. The asymptotic properties of the pooling resampling-based method are first investigated. A novel Monte Carlo evaluation procedure namely the n-times pooling resampling-based method is then proposed. Theorems as well as simulations show that the proposed method can give smaller or comparable root mean squared errors and bias with much less computing time, thus can be strongly recommended especially for evaluating highly computationally intensive hypothesis testing procedures.