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首页> 外文学位 >Skart: A skewness- and autoregression-adjusted batch-means procedure for simulation analysis.
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Skart: A skewness- and autoregression-adjusted batch-means procedure for simulation analysis.

机译：Skart：用于偏斜和自回归调整的批量方法，用于模拟分析。

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摘要

We discuss Skart, an automated batch-means procedure for constructing a skewness- and autoregression-adjusted confidence interval (CI) for the steady-state mean of a simulation output process in either discrete time (i.e., observation-based statistics) or continuous time (i.e., time-persistent statistics). Skart is a sequential procedure designed to deliver a CI that satisfies user-specified requirements concerning not only the CI's coverage probability but also the absolute or relative precision provided by its half-length. Skart exploits separate adjustments to the half-length of the classical batchmeans CI so as to account for the effects on the distribution of the underlying Student's t-statistic that arise from skewness (nonnormality) and autocorrelation of the batch means. The skewness adjustment is based on a modified Cornish-Fisher expansion for the classical batch-means Student's t -ratio, and the autocorrelation adjustment is based on an autoregressive approximation to the batch-means process for sufficiently large batch sizes. Skart also delivers a point estimator for the steady-state mean that is approximately free of initialization bias. The duration of the associated warm-up period (i.e., the statistics clearing time) is based on iteratively applying von Neumann's randomness test to spaced batch means with progressively increasing batch sizes and interbatch spacer sizes. In an experimental performance evaluation involving a wide range of test processes, Skart compared favorably with other simulation analysis methods---namely, its predecessors ASAP3, WASSP, and SBatch as well as ABATCH, LBATCH, the Heidelberger-Welch procedure, and the Law-Carson procedure. Specifically, Skart exhibited competitive sampling efficiency and substantially closer conformance to the given CI coverage probabilities than the other procedures.;Also presented is a nonsequential version of Skart, called N-Skart, in which the user supplies a single simulation-generated series of arbitrary length and specifies a coverage probability for a CI based on that series. In the same set of test processes previously mentioned and for a range of data-set sizes, N-Skart also achieved close conformance to the specified CI coverage probabilities.
机译：我们讨论Skart，这是一种自动批处理程序，可为离散时间（即基于观测的统计数据）或连续时间中的模拟输出过程的稳态均值构造偏度和自回归调整的置信区间（CI） （即持续时间统计）。 Skart是一种顺序过程，旨在提供可满足用户指定要求的CI，不仅涉及CI的覆盖概率，还涉及其半长所提供的绝对或相对精度。 Skart利用对经典批处理均值CI的一半长度的单独调整，以解决因批处理偏斜（非正态）和自相关而对基础学生t统计量的分布的影响。偏度调整基于经典的批处理均方学生t比率的改良Cornish-Fisher展开，而自相关调整基于批处理均方根过程的自回归近似（对于足够大的批处理大小）。 Skart还为稳态均值提供了一个点估计器，该估计器几乎没有初始化偏差。相关的预热时间（即统计数据清除时间）的持续时间是基于将冯·诺依曼的随机性测试迭代应用到间隔批处理中，并逐渐增加批大小和批间间隔符大小。在涉及广泛测试过程的实验性能评估中，Skart与其他模拟分析方法（即其前身ASAP3，WASSP和SBatch以及ABATCH，LBATCH，Heidelberger-Welch程序和法律）相比具有优势。卡森程序。具体来说，Skart展示了具有竞争性的采样效率，并且与其他过程相比，与给定的CI覆盖率具有显着的一致性。还介绍了Skart的非顺序版本，称为N-Skart，用户可在其中提供一个由仿真生成的任意序列长度，并指定基于该序列的CI的覆盖概率。在前面提到的同一组测试过程中，对于一定范围的数据集大小，N-Skart还实现了与指定CI覆盖概率的高度一致性。

著录项

  • 作者

    Tafazzoli Yazdi, Ali.;

  • 作者单位

    North Carolina State University.;

  • 授予单位 North Carolina State University.;
  • 学科 Statistics.;Physics Elementary Particles and High Energy.;Engineering Industrial.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 147 p.
  • 总页数 147
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 统计学;一般工业技术;高能物理学;
  • 关键词

  • 入库时间 2022-08-17 11:38:30

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