Orthogonal Bootstrap: Efficient Simulation of Input Uncertainty

arXiv:2404.19145v1 Announce Type: cross
Abstract: Bootstrap is a popular methodology for simulating input uncertainty. However, it can be computationally expensive when the number of samples is large. We propose a new approach called \textbf{Orthogonal Bootstrap} that reduces the number of required Monte Carlo replications. We decomposes the target being simulated into two parts: the \textit{non-orthogonal part} which has a closed-form result known as Infinitesimal Jackknife and the \textit{orthogonal part} which is easier to be simulated. We theoretically and numerically show …

abstract arxiv bootstrap cs.lg econ.em however math.st methodology popular samples simulation stat.me stat.ml stat.th type uncertainty