title: Efficient Monte Carlo evaluation of resampling-based hypothesis tests with applications to genetic epidemiology.
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. In this article, we first investigate the asymptotic properties of the pooling resampling-based method and then propose a novel Monte Carlo evaluation procedure namely the n-times pooling resampling-based method. 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 in genetic epidemiology.
authors: 1 Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong.
authors: Wing K
authors: WK
authors: Fung
authors: 1 Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong.
authors: Kexin
authors: K
authors: Yu
authors: 1 Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong.
authors: Yingrui
authors: Y
authors: Yang
authors: 2 State Key Laboratory of Organ Failure Research, Ministry of Education; Guangdong Provincial Key Laboratory of Tropical Research, Department of Biostatistics, School of Public Health, Southern Medical University, China.
authors: Ji-Yuan
authors: JY
authors: Zhou
mesh_terms: D015982:Bias; D006801:Humans; D015810:Linkage Disequilibrium; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D017720:Molecular Epidemiology / Q000379:methods*; D009010:Monte Carlo Method; D018401:Sample Size; D012494:Sampling Studies
keywords: Bootstrap test; Monte Carlo sample; permutation test; power estimate; resampling