Bootstrap Confidence Regions for the Intensity of a Poisson Point Process
为非平稳泊松过程的强度函数构建置信区域，提出多种重抽样算法，包括基于非参数估计强度和直接重抽样数据点的方法，并比较不同百分位t置信带的性能。
Abstract Bootstrap methods are developed for constructing confidence regions for the intensity function of a nonstationary Poisson process. Several different resampling algorithms are suggested, ranging from resampling a Poisson process with intensity equal to that estimated nonparametrically from the data to resampling the data points themselves in the same manner that the bootstrap is used in problems involving independent and identically distributed observations. For each different bootstrap method, various percentile-t ways of constructing confidence bands are described. The effectiveness of these different approaches is demonstrated both theoretically and numerically, for real and simulated data. Issues such as bias correction are addressed.