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Modern design of experiments methods for screening and experimentations with mixture and qualitative variables.

机译：用于筛选和混合和定性变量的实验方法的现代设计。

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摘要

This dissertation re-examines some of the most basic design of experiment methods with respect to their ability to achieve intuitive objectives. For example, it provides probably the first comprehensive evaluation of the ability of standard screening approaches to correctly tell which factors have important effects on average outputs. Also, the dissertation examines the prediction errors that users of so-called mixture experimental design and qualitative response surface methods can achieve.;In practical situations, the derived "decision support " information can tell the user in advance whether the number of runs used is adequate for the experimenter's needs and provide a basis for selecting one method over another when alternatives are presented. Also, the dissertation clarifies, perhaps for the first time, the potentially serious prediction error issues associated with the methods that have been proposed for response surface investigation when some factors are qualitative.;In addition to developing comprehensive computational studies of existing methods, new methods are proposed with potentially important advantages. For example, the dissertation provides some of the first unbalanced screening experimental plans relevant to cases in which some combinations of settings have far higher costs than other combinations.;For situations in which some factors are mixture components, e.g., %CO 2, %Ar, %N, and other factors are process variables, the dissertation provides some of the first economically relevant experimental plans offering potentially substantial reductions in prediction errors. Also, the dissertation provides the first truly advisable experimental designs for many response surface cases in which some variables are qualitative.;All new methods are derived from optimization formulations or "improvement systems design problems". In each case, the intent is to design the method using the objective or objectives that most directly describe the purpose of the improvement system. Also, the formulations build on the most realistic, concise assumption schemes in the applied statistics literature.
机译：本文就实现直观目标的能力重新审视了一些最基本的实验方法设计。例如，它可能是对标准筛选方法正确识别哪些因素对平均产出产生重要影响的能力的首次全面评估。此外，本文还研究了所谓的混合实验设计和定性响应面方法用户可以实现的预测误差。；在实际情况下，派生的“决策支持”信息可以提前告知用户使用的运行次数是否为足以满足实验者的需求，并为在提出替代方案时选择一种方法而不是另一种方法提供了基础。同样，本文也许是首次澄清了在某些因素定性的情况下与响应面调查所提出的方法相关的潜在的严重预测误差问题。除了开展对现有方法的综合计算研究外，新方法建议具有潜在的重要优势。例如，本文提供了一些第一个不平衡筛选实验计划，这些计划与某些设置组合的成本远高于其他组合的情况有关；对于某些因素是混合成分的情况，例如％CO 2，％Ar ，％N和其他因素是过程变量，因此本文提供了一些与经济相关的第一个实验计划，从而可以大大降低预测误差。此外，本文还为许多响应表面情况（其中某些变量是定性的）提供了第一个真正可取的实验设计。所有新方法均来自优化公式或“改进系统设计问题”。在每种情况下，目的都是使用最直接描述改进系统目的的一个或多个目标来设计方法。同样，这些公式建立在应用统计文献中最现实，最简洁的假设方案的基础上。

著录项

  • 作者

    Chantarat, Navara.;

  • 作者单位

    The Ohio State University.;

  • 授予单位 The Ohio State University.;
  • 学科 Industrial engineering.;Statistics.;Operations research.
  • 学位 Ph.D.
  • 年度 2003
  • 页码 133 p.
  • 总页数 133
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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