Nonstandard M-estimation can be tricky because the estimators often don't follow the usual rules. But using m out of n bootstrapping can help estimate their sampling distributions consistently. This method works well for various types of M-estimators, like those used in regression analysis. The researchers showed that m out of n bootstrapping is a reliable way to estimate the sampling distributions of these estimators, even when they don't behave like normal distributions. This approach can be used to create confidence intervals and get a better understanding of how these estimators work in real-world situations.