Variance of the Channelized-Hotelling Observer from a Finite Number of Trainers and Testers

摘要

In this paper we analyze the bootstrap and shuffle methods for estimating the mean and variance of the performance of the channelized-Hotelling observer given a finite number of images for training and testing. This background is needed to understand the role of the bootstrap and shuffle methods in new and more complicated models of bias and variance. We assess the accuracy and precision of the bootstrap and shuffle estimates by comparing them to Monte Carlo estimates. The comparisons show that the shuffle estimate of the mean and the bootstrap estimate of the variance are unbiased.