摘要翻译：
提出了一种基于适当选择发散/收敛统计量的样本内演化的通用速率估计方法。所提出的速率估计器基于简单的最小二乘参数，并且在不需要选择调谐参数的情况下，在非常一般的情况下是准确的。引入扫描的概念，从数据序列中提取有用的子样本；将所提出的速率估计方法应用于不同的扫描，然后将得到的估计量组合起来以提高精度。讨论了在重尾指数估计和长记忆参数估计问题中的应用；一个小的模拟研究补充了我们的理论结果。
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英文标题：
《Computer-intensive rate estimation, diverging statistics and scanning》
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作者：
Tucker McElroy, Dimitris N. Politis
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最新提交年份：
2007
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分类信息：

一级分类：Mathematics        数学
二级分类：Statistics Theory        统计理论
分类描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计：例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
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一级分类：Statistics        统计学
二级分类：Statistics Theory        统计理论
分类描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近，贝叶斯推论，决策理论，估计，基础，推论，检验。
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英文摘要：
  A general rate estimation method is proposed that is based on studying the in-sample evolution of appropriately chosen diverging/converging statistics. The proposed rate estimators are based on simple least squares arguments, and are shown to be accurate in a very general setting without requiring the choice of a tuning parameter. The notion of scanning is introduced with the purpose of extracting useful subsamples of the data series; the proposed rate estimation method is applied to different scans, and the resulting estimators are then combined to improve accuracy. Applications to heavy tail index estimation as well as to the problem of estimating the long memory parameter are discussed; a small simulation study complements our theoretical results.
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PDF链接：
https://arxiv.org/pdf/710.5004