Smoothed Bootstrap Methods for Hypothesis Testing

作者：Asamh Saleh M. Al Luhayb, Tahani Coolen‐Maturi, Frank P. A. Coolen · 发表于：Journal of Statistical Theory and Practice · 年份：2024 · DOI：10.1007/s42519-024-00370-x · 被引用次数：4 · 研究领域：Fault Detection and Control Systems、Statistical Methods and Inference、Gaussian Processes and Bayesian Inference

Abstract This paper demonstrates the application of smoothed bootstrap methods and Efron’s methods for hypothesis testing on real-valued data, right-censored data and bivariate data. The tests include quartile hypothesis tests, two sample medians and Pearson and Kendall correlation tests. Simulation studies indicate that the smoothed bootstrap methods outperform Efron’s methods in most scenarios, particularly for small datasets. The smoothed bootstrap methods provide smaller discrepancies between the actual and nominal error rates, which makes them more reliable for testing hypotheses.