Ever since its introduction, the bootstrap has provided both a powerful set of solutions for
practical statisticians, and a rich source of theoretical and methodological problems for …

Understanding and mitigating SARS-CoV-2 transmission hinges on antibody and viral RNA
data that inform exposure and shedding, but extensive variation in assays, study group …

We present an adaptive method for the automatic scaling of random-walk Metropolis–
Hastings algorithms, which quickly and robustly identifies the scaling factor that yields a …

The parametric and nonparametric approaches to the bootstrap are compared as to their
performance in estimating uncertainties in extreme-value models. Simulation experiments …

Resampling methods including randomization test, cross‐validation, the jackknife and the
bootstrap are widely employed in the research areas of natural science, engineering and …

Bootstrap methods are attractive empirical procedures for assessment of errors in problems
of statistical estimation, and allow highly accurate inference in a vast range of parametric …

Many contemporary classifiers are constructed to provide good performance for very high
dimensional data. However, an issue that is at least as important as good classification is …

Our ability to understand and mitigate the spread of SARS-CoV-2 depends largely on
antibody and viral RNA data that provide information about past exposure and shedding …

The standard forms of bootstrap iteration are very computationally demanding. As a result,
there have been several attempts to alleviate the computational burden by use of …

The oldest stochastic approximation method is the Robbins–Monro process. This estimates
an unknown scalar parameter by stepping from one trial value for the parameter to another …