General minimum lower-order confounding three-level split-plot designs when the whole plot factors are important

IF 0.8 4区 数学 Q3 STATISTICS & PROBABILITY Canadian Journal of Statistics-Revue Canadienne De Statistique Pub Date : 2022-11-15 DOI:10.1002/cjs.11744
Tao Sun, Shengli Zhao
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引用次数: 1

Abstract

Three-level fractional factorial split-plot (FFSP) designs with the whole plot (WP) factors being more important than the subplot factors are considered in the article. An aliased component-number pattern of type WP (WP-ACNP) is introduced for ranking such designs. The criterion of general minimum lower-order confounding of type WP (WP-GMC) is proposed based on WP-ACNP. The expressions of the key components in WP-ACNP are given via complementary sets. Some necessary conditions for FFSP designs to be WP-GMC FFSP designs are given and some three-level WP-GMC FFSP designs are constructed and tabulated.

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当整个地块因素很重要时,一般最小低阶混淆三级分割地块设计
本文考虑了整体因子比次要因子更重要的三水平分数因子分割图(FFSP)设计。引入了WP类型的别名组件数模式(WP- acnp)对此类设计进行排序。提出了基于WP- acnp的WP型一般最小低阶混杂判据(WP- gmc)。利用互补集给出了WP-ACNP中关键分量的表达式。给出了FFSP设计成为WP-GMC型FFSP设计的必要条件,并构造了一些三级WP-GMC型FFSP设计。
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来源期刊
CiteScore
1.40
自引率
0.00%
发文量
62
审稿时长
>12 weeks
期刊介绍: The Canadian Journal of Statistics is the official journal of the Statistical Society of Canada. It has a reputation internationally as an excellent journal. The editorial board is comprised of statistical scientists with applied, computational, methodological, theoretical and probabilistic interests. Their role is to ensure that the journal continues to provide an international forum for the discipline of Statistics. The journal seeks papers making broad points of interest to many readers, whereas papers making important points of more specific interest are better placed in more specialized journals. The levels of innovation and impact are key in the evaluation of submitted manuscripts.
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Issue Information True and false discoveries with independent and sequential e-values Issue Information Multiple change-point detection for regression curves Robust estimation of loss-based measures of model performance under covariate shift
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