On the Bootstrap and Likelihood-Based Confidence Regions
描述了一种利用自助法和非参数密度估计构建向量参数似然置信区域的方法，并通过数值例子说明其理论性质，同时给出了构建多元参数分量同时置信区间的自助算法。
We describe a method for constructing likelihood-based confidence regions for a vector parameter, using the bootstrap and nonparametric density estimation. The technique is illustrated by application to a numerical example, and its theoretical properties are elucidated. It is argued that likelihood-based regions should not be approximated by ellipses, if we are to have any hope of capturing first-order departures from normality. Bootstrap algorithms for constructing simultaneous confidence intervals for the components of a vector parameter are also presented. Advantages of the percentile-t method over the ordinary percentile method are demonstrated in a multivariate setting.