文章基本信息

标题：An empirical comparison of block bootstrap methods: traditional and newer ones
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作者：Beste H. Beyaztas ; Esin Firuzan
期刊名称：Journal of Data Science
印刷版ISSN：1680-743X
电子版ISSN：1683-8602
出版年度：2016
卷号：14
期号：4
页码：641-656
出版社：Tingmao Publish Company
摘要：In this study, we compared various block bootstrap methods in terms of parameter estimation, biases and mean squared errors (MSE) of the bootstrap estimators. Comparison is based on four real-world examples and an extensive simulation study with various sample sizes, parameters and block lengths. Our results reveal that ordered and sufficient ordered non-overlapping block bootstrap methods proposed by Beyaztas et al. (2016) provide better results in terms of parameter estimation and its MSE compared to conventional methods. Also, sufficient non-overlapping block bootstrap method and its ordered version have the smallest MSE for the sample mean among the others.
关键词：Block bootstrap; bootstrap; estimation; linear time series; sufficient bootstrap