We propose a double bootstrap procedure for reducing coverage error in the confidence intervals of descriptive statistics for independent and identically distributed functional data. Through a series of Monte Carlo simulations, we compare the finite sample performance of single and double bootstrap procedures for estimating the distribution of descriptive statistics for independent and identically distributed functional data. At the cost of longer computational time, the double bootstrap with the same bootstrap method reduces confidence level error and provides improved coverage accuracy than the single bootstrap. Illustrated by a Canadian weather station data set, the double bootstrap procedure presents a tool for visualising the distribution of the descriptive statistics for the functional data.

翻译：我们为独立和相同分布功能数据的描述性统计,提议了减少描述性统计信任期误差的双靴套程序。通过一系列蒙特卡洛模拟,我们比较了用于估计独立和相同分布功能数据描述性统计分布的单一和双靴套程序的有限抽样性能。用同一靴套方法的双靴套在计算时间较长的情况下,减少了信任度误差,使覆盖面比单靴套更准确。由加拿大气象站数据集推算,双靴套程序为功能性数据描述性统计数据的可视分布提供了一种工具。