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Analysis of data from orthogonal minimally aliased response surface designs 正交最小混叠响应面设计数据分析
IF 2.5 2区 工程技术 Q2 ENGINEERING, INDUSTRIAL Pub Date : 2023-01-23 DOI: 10.1080/00224065.2022.2151530
Mohammed Saif Ismail Hameed, José Núñez Ares, P. Goos
Abstract Experimental data are often highly structured due to the use of experimental designs. This does not only simplify the analysis, but it allows for tailored methods of analysis that extract more information from the data than generic methods. One group of three-level experimental designs that are suitable for such tailored methods are orthogonal minimally aliased response surface (OMARS) designs (Núñez Ares and Goos 2020), where all main effects are orthogonal to each other and to all second-order effects. The design based analysis method of Jones and Nachtsheim (2017) has shown significant improvement over existing methods in terms of powers to detect active effects. However, the application of their method is limited to only a small subgroup of OMARS designs known as definitive screening designs (DSDs). In our work, we not only improve upon the Jones and Nachtsheim method for DSDs, but we also generalize their analysis framework to the entire family of OMARS designs. Using extensive simulations, we show that our customized method for analyzing data from OMARS designs is highly effective in identifying active effects when compared to other modern (non-design based) analysis methods, especially in cases where the true model is complex and involves many second-order effects.
由于实验设计的使用,实验数据往往是高度结构化的。这不仅简化了分析,而且允许定制分析方法,从数据中提取比一般方法更多的信息。适合这种定制方法的一组三水平实验设计是正交最小混叠响应面(OMARS)设计(Núñez Ares和Goos 2020),其中所有主要效应彼此正交,并与所有二阶效应正交。Jones和Nachtsheim(2017)基于设计的分析方法在检测主动效应的能力方面比现有方法有了显着改进。然而,他们的方法的应用仅限于被称为确定筛选设计(dsd)的OMARS设计的一小部分亚组。在我们的工作中,我们不仅改进了dsd的Jones和Nachtsheim方法,而且我们还将其分析框架推广到整个OMARS设计家族。通过广泛的模拟,我们表明,与其他现代(非基于设计的)分析方法相比,我们用于分析OMARS设计数据的定制方法在识别主动效应方面非常有效,特别是在真实模型复杂且涉及许多二阶效应的情况下。
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引用次数: 1
Lot acceptance testing using sample mean and extremum with finite qualification samples 使用有限合格样本的样本均值和极值进行批量验收测试
IF 2.5 2区 工程技术 Q2 ENGINEERING, INDUSTRIAL Pub Date : 2023-01-20 DOI: 10.1080/00224065.2022.2147884
S. Kloppenborg
Abstract In the aerospace composites industry, new material lots are tested to determine if they are suitable for use. It is common to accept or reject the material lot by comparing the sample mean and lower extremum to reference values that are established based on an initial (qualification) sample of material property measurements. Current industry practices assume that the samples are drawn from a normal distribution with known parameters equal to the mean and standard deviation of the qualification sample: this assumption yields a producer’s risk that is too high. This article presents a two-sample method of setting these reference values, considering the sampling distribution of the qualification sample. This new method is validated through simulation which shows that it produces the correct probability of Type I error. Simulation is also used to investigate the statistical power of the new method and it is compared to others commonly used. A case study is presented to demonstrate the use of the new method using composite material data from industry.
在航空航天复合材料工业中,对新材料批次进行测试以确定其是否适合使用。通过将样品平均值和下限值与基于材料性能测量的初始(合格)样品建立的参考值进行比较,通常可以接受或拒绝材料批次。目前的行业实践假设样本是从正态分布中提取的,已知参数等于合格样本的平均值和标准偏差:这种假设导致生产者的风险太高。考虑到合格样本的抽样分布,本文提出了一种设置这些参考值的双样本方法。通过仿真验证了该方法的有效性,结果表明该方法产生的I类误差概率是正确的。仿真研究了新方法的统计能力,并与其他常用方法进行了比较。最后以复合材料工业数据为例,说明了该方法的应用。
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引用次数: 0
Constructing control charts for autocorrelated data using an exhaustive systematic samples pooled variance estimator 使用穷举系统样本池方差估计器构建自相关数据的控制图
IF 2.5 2区 工程技术 Q2 ENGINEERING, INDUSTRIAL Pub Date : 2023-01-12 DOI: 10.1080/00224065.2022.2148590
S. Grimshaw
Abstract SPC with positive autocorrelation is well known to result in frequent false alarms if the autocorrelation is ignored. The autocorrelation is a nuisance and not a feature that merits modeling and understanding. This paper proposes exhaustive systematic sampling, which is similar to Bayesian thinning except no observations are dropped, to create a pooled variance estimator that can be used in Shewhart control charts with competitive performance. The expected value and variance are derived using quadratic forms that is nonparametric in the sense no distribution or time series model is assumed. Practical guidance for choosing the systematic sampling interval is offered to choose a value large enough to be approximately unbiased and not too big to inflate variance. The proposed control charts are compared to time series residual control charts in a simulation study that validates using the empirical reference distribution control limits to preserve stated in-control false alarm probability and demonstrates similar performance.
具有正自相关的SPC如果忽略自相关,会导致频繁的虚警。自相关是一种麻烦,不值得建模和理解的特征。本文提出了穷尽系统抽样,它类似于贝叶斯稀疏,只是没有丢弃观测值,以创建一个可用于具有竞争性能的Shewhart控制图的池方差估计器。期望值和方差使用二次型推导,在没有假设分布或时间序列模型的意义上是非参数的。为选择系统抽样间隔提供了实用指导,以选择一个足够大的值,以近似无偏,而不是太大,以膨胀方差。在仿真研究中,将所提出的控制图与时间序列残差控制图进行了比较,验证了使用经验参考分布控制极限来保持所述的控制虚警概率,并展示了相似的性能。
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引用次数: 1
Efficient analysis of split-plot experimental designs using model averaging 用模型平均法对分裂图实验设计进行有效分析
IF 2.5 2区 工程技术 Q2 ENGINEERING, INDUSTRIAL Pub Date : 2023-01-12 DOI: 10.1080/00224065.2022.2147108
C. Y. Hong, D. Fletcher, Jiaxu Zeng, C. McGraw, C. Cornwall, V. Cummings, N. Barr, Emily J. Frost, P. Dillingham
Abstract Split-plot experimental data are often analyzed as if the data came from a completely randomized design. As is well known, ignoring the different levels of randomization and replication can lead to serious inferential errors. However, in some experiments, including many of the ocean global change experiments that motivated this research, variation between whole-plot experimental units may be small relative to variation between subplot units. Even though a factorial analysis will often perform poorly in general, in this special case it outperforms a split-plot analysis, providing narrower confidence intervals for treatment means and differences with coverage rates close to the desired level. The performance of the proposed model-averaged analysis was compared to a classical split-plot analysis via a simulation study, and its utility demonstrated for an ocean global change experiment examining growth and condition of a juvenile mussel species. In our simulation study, model-averaged confidence intervals for whole-plot treatment means or comparisons of means were up to 40% narrower than split-plot confidence intervals while maintaining close to nominal coverage rates. In our example experiment, we observed narrowing of up to 25%. We recommend model averaging as a preferred approach when variation between whole-plot experimental units is expected to be less than between subplot units, with a few caveats for studies with very few replicates.
拆分图实验数据通常被当作完全随机设计的数据来分析。众所周知,忽略不同程度的随机化和复制会导致严重的推论错误。然而,在一些实验中,包括许多推动本研究的海洋全球变化实验,整体实验单元之间的差异可能相对于子单元之间的差异较小。尽管析因分析通常表现不佳,但在这种特殊情况下,它优于分裂图分析,为治疗手段和覆盖率接近所需水平的差异提供了更窄的置信区间。通过模拟研究,将所提出的模型平均分析的性能与经典的分裂图分析进行了比较,并在研究幼年贻贝物种生长和状况的海洋全球变化实验中证明了其实用性。在我们的模拟研究中,整体图处理均值或均值比较的模型平均置信区间比分割图置信区间窄40%,同时保持接近名义覆盖率。在我们的示例实验中,我们观察到的变窄高达25%。当整块实验单元之间的差异小于子块单元之间的差异时,我们推荐模型平均作为首选方法,对于重复很少的研究有一些注意事项。
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引用次数: 0
Message from the Editor 编辑留言
IF 2.5 2区 工程技术 Q2 ENGINEERING, INDUSTRIAL Pub Date : 2023-01-01 DOI: 10.1080/00224065.2023.2160575
Allison Jones-Farmer
papillomavirus
乳头瘤病毒
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引用次数: 0
Building a Platform for Data-Driven Pandemic Prediction from Data Modeling to Visualization – The CovidLP Project 构建从数据建模到可视化的数据驱动大流行预测平台——covid - lp项目
IF 2.5 2区 工程技术 Q2 ENGINEERING, INDUSTRIAL Pub Date : 2023-01-01 DOI: 10.1080/00224065.2022.2041377
Joseph David Reviewer: Conklin
Here is the book for everyone in our field who has ever been asked about the meaning and value of statistics in the modern world. This is the book to give to your nonstatistician acquaintances the next time you get this question. For the rest of us, read this book and buckle up for an exciting adventure in the advance of datadriven knowledge and interdisciplinary collaboration. The book portrays, in admirable sweep and detail, the tireless efforts of statisticians and computer scientists under the auspices of the University of Brazil. Their goal is to create, maintain, and advance an online platform for COVID-19 pandemic prediction, one that can run on computers, notebooks, tablets, and mobile phones. Their aim is nothing less than a platform for predicting pandemic infections and deaths both in the short term—up to two weeks—and in the long term—until the end of the current wave of COVID-19 within a given state, region, and ultimately any country on the planet. The adventure plays out in 17 chapters:
这本书是为我们这个领域的每一个曾经被问到现代世界统计的意义和价值的人写的。下次你们遇到这个问题的时候可以把这本书给你们的非统计学家朋友。对于我们其余的人来说,阅读这本书,并在数据驱动的知识和跨学科合作的进步中做好准备进行令人兴奋的冒险。这本书以令人钦佩的全面和细节描绘了巴西大学(University of Brazil)赞助下统计学家和计算机科学家的不懈努力。他们的目标是创建、维护和推进一个可以在电脑、笔记本电脑、平板电脑和手机上运行的COVID-19大流行预测在线平台。他们的目标是建立一个预测大流行感染和死亡的平台,无论是在短期内(最多两周),还是在长期内(直到当前一波COVID-19在特定州、地区乃至最终在地球上任何国家结束)。这次冒险共分17章:
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引用次数: 2
Augmenting definitive screening designs: Going outside the box 增强明确的放映设计:跳出框框
IF 2.5 2区 工程技术 Q2 ENGINEERING, INDUSTRIAL Pub Date : 2022-11-03 DOI: 10.1080/00224065.2022.2128946
Mengmeng Liu, Robert W. Mee, Yongdao Zhou
Abstract Definitive screening designs (DSDs) have grown rapidly in popularity since their introduction by Jones and Nachtsheim (2011). Their appeal is that the second-order response surface (RS) model can be estimated in any subset of three factors, without having to perform a follow-up experiment. However, their usefulness as a one-step RS modeling strategy depends heavily on the sparsity of second-order effects and the dominance of first-order terms over pure quadratic terms. To address these limitations, we show how viewing a projection of the design region as spherical and augmenting the DSD with axial points in factors found to involve second-order effects remedies the deficiencies of a stand-alone DSD. We show that augmentation with a second design consisting of axial points is often the D s -optimal augmentation, as well as minimizing the average prediction variance. Supplemented by this strategy, DSDs are highly effective initial screening designs that support estimation of the second-order RS model in three or four factors.
自Jones和Nachtsheim(2011)提出明确筛选设计(dds)以来,dds迅速流行起来。他们的吸引力在于二阶响应面(RS)模型可以在三个因素的任何子集中进行估计,而无需进行后续实验。然而,它们作为一步RS建模策略的有用性在很大程度上取决于二阶效应的稀疏性和一阶项相对于纯二次项的优势。为了解决这些限制,我们展示了如何将设计区域的投影视为球形,并在涉及二阶效应的因素中使用轴向点来增加DSD,以弥补独立DSD的不足。我们表明,由轴点组成的第二种设计的增强通常是最优的增强,以及最小化平均预测方差。在此策略的补充下,dsd是一种非常有效的初始筛选设计,支持在三到四个因素中估计二阶RS模型。
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引用次数: 0
Monitoring and diagnostics of correlated quality variables of different types 不同类型相关质量变量的监测与诊断
IF 2.5 2区 工程技术 Q2 ENGINEERING, INDUSTRIAL Pub Date : 2022-08-22 DOI: 10.1080/00224065.2022.2109533
Wei-Heng Huang, Jing Sun, A. Yeh
Abstract As data acquisition and processing technologies continue to advance rapidly, new challenges emerge for statistical process monitoring. One such challenge, especially in the era of big data analytics, is monitoring multivariate processes involving a mixture of continuous, categorical, and discrete quality variables. The existing multivariate control charts focus mostly on monitoring correlated variables of the same type. We propose a new Phase II control chart that is based on a modified Holm’s step-down multiple testing procedure (Holm 1979) which achieves two important goals at the same time: (1) it simultaneously monitors correlated variables of different types, while keeping the probability of false alarm under desirable level, and (2) when the process is determined to be out of control, it further provides, without any additional efforts, diagnostics to pinpoint which parameters are out of control. The proposed chart is shown to outperform the existing charts particularly in its ability to provide more accurate diagnostics.
随着数据采集和处理技术的快速发展,统计过程监控面临着新的挑战。其中一个挑战,特别是在大数据分析时代,是监测涉及连续、分类和离散质量变量混合的多变量过程。现有的多变量控制图多侧重于监测同类型的相关变量。我们提出了一个新的II期控制图,该控制图基于改进的Holm降压多重测试程序(Holm 1979),同时实现了两个重要目标:(1)它同时监测不同类型的相关变量,同时保持误报警的概率在理想的水平下;(2)当确定过程失控时,它进一步提供诊断,而无需任何额外的努力,以查明哪些参数失控。所提出的图表优于现有图表,特别是在提供更准确诊断的能力方面。
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引用次数: 0
Analyzing dispersion effects from replicated order-of-addition experiments 从重复的加阶实验分析色散效应
IF 2.5 2区 工程技术 Q2 ENGINEERING, INDUSTRIAL Pub Date : 2022-08-22 DOI: 10.1080/00224065.2022.2110024
Shin-Fu Tsai
Abstract Dispersion effects may play a vital role, in addition to location effects, in exploring optimal addition orders of several materials in some chemical, industrial and pharmaceutical studies. Two replication-based statistical methods developed using frequentist and fiducial probability arguments are introduced in this paper to identify active dispersion effects from replicated order-of-addition experiments. Simulation results show that both approaches can maintain empirical sizes sufficiently close to the nominal level while their finite-sample performances are very similar. From a statistical perspective, the fiducial method can provide a unified probability framework to analyze dispersion effects as well as location effects. However, it is computationally more expensive than the frequentist method. Consequently, the frequentist method is recommended for real-world applications due to its low computational cost. A drug combination study is used to illustrate these two approaches. In addition, some eligible order-of-addition designs are collected in a catalogue for future work.
摘要:在一些化学、工业和制药研究中,除位置效应外,分散效应在寻找几种材料的最佳加成顺序方面可能起着至关重要的作用。本文介绍了两种基于复制的统计方法,使用频率论和基准概率参数来识别复制的加阶实验的主动色散效应。仿真结果表明,两种方法都能保持足够接近名义水平的经验尺寸,而它们的有限样本性能非常相似。从统计学的角度来看,基准法可以提供一个统一的概率框架来分析色散效应和位置效应。然而,它在计算上比频率论方法更昂贵。因此,由于计算成本低,频率方法被推荐用于实际应用。一项药物联合研究被用来说明这两种方法。此外,还将一些符合条件的附加顺序设计收集在目录中,以供今后的工作使用。
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引用次数: 2
In-profile monitoring for cluster-correlated data in advanced manufacturing system 先进制造系统中集群相关数据的剖面内监测
IF 2.5 2区 工程技术 Q2 ENGINEERING, INDUSTRIAL Pub Date : 2022-08-16 DOI: 10.1080/00224065.2022.2106912
Peiyao Liu, Juan Du, Yangyang Zang, Chen Zhang, Kaibo Wang
Abstract Nowadays advanced sensing technology enables real-time data collection of key variables during manufacturing, known as multi-channel profiles. These data facilitate in-process monitoring and anomaly detection, which have been extensively studied in recent years. However, most studies treat each profile as a whole, e.g., a high-dimensional vector or function, and construct monitoring schemes accordingly. As a result, these methods cannot be implemented until the entire profile has been obtained, leading to long detection delay especially if anomalies occur in early sensing points of the profile. In addition, they require that profiles of different samples have the same time length and feature location, yet additional time-warping operation for real misaligned samples may weaken the anomaly patterns. To address these problems, this article proposes an in-profile monitoring (INPOM) control chart, which not only gives the feasibility of detecting anomalies inside the profile, but also can handle the misalignment problem of different samples. In particular, our INPOM scheme is built upon state space model (SSM). To better describe the clustered between-profile correlation and avoid overfitting, SSM is extended to a regularized SSM (RSSM), where regularizations are imposed as prior information and expectation maximization algorithm is integrated for posterior maximization to efficiently learn the model parameters. Furthermore, a monitoring statistic based on one-step-ahead prediction error of RSSM is constructed for INPOM control chart. Thorough numerical studies and real case studies demonstrate the effectiveness and applicability of our proposed RSSM-INPOM framework.
如今,先进的传感技术能够实时收集制造过程中的关键变量数据,称为多通道剖面。这些数据有助于过程监控和异常检测,近年来得到了广泛的研究。然而,大多数研究将每个剖面视为一个整体,例如高维向量或函数,并相应地构建监测方案。因此,在获得整个剖面之前,这些方法无法实施,导致检测延迟很长,特别是在剖面的早期传感点出现异常时。此外,它们要求不同样本的剖面具有相同的时间长度和特征位置,而对真正的不对齐样本进行额外的时间翘曲操作可能会削弱异常模式。针对这些问题,本文提出了一种轮廓内监测(INPOM)控制图,该控制图不仅提供了轮廓内异常检测的可行性,而且可以处理不同样本的不对中问题。特别是,我们的INPOM方案是建立在状态空间模型(SSM)之上的。为了更好地描述聚类的剖面间相关性并避免过拟合,将SSM扩展为正则化SSM (RSSM),其中正则化作为先验信息,并集成期望最大化算法进行后验最大化,以有效地学习模型参数。在此基础上,对INPOM控制图构建了基于RSSM超前一步预测误差的监测统计量。深入的数值研究和实际案例研究证明了我们提出的RSSM-INPOM框架的有效性和适用性。
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引用次数: 0
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Journal of Quality Technology
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