Journal of Quality Technology最新文献

{"title":"funcharts: control charts for multivariate functional data in R","authors":"Christian Capezza, Fabio Centofanti, A. Lepore, A. Menafoglio, B. Palumbo, S. Vantini","doi":"10.1080/00224065.2023.2219012","DOIUrl":"https://doi.org/10.1080/00224065.2023.2219012","url":null,"abstract":"Modern statistical process monitoring (SPM) applications focus on profile monitoring, i.e., the monitoring of process quality characteristics that can be modeled as profiles, also known as functional data. Despite the large interest in the profile monitoring literature, there is still a lack of software to facilitate its practical application. This article introduces the funcharts R package that implements recent developments on the SPM of multivariate functional quality characteristics, possibly adjusted by the influence of additional variables, referred to as covariates. The package also implements the real-time version of all control charting procedures to monitor profiles partially observed up to an intermediate domain point. The package is illustrated both through its built-in data generator and a real-case study on the SPM of Ro-Pax ship CO2 emissions during navigation, which is based on the ShipNavigation data provided in the Supplementary Material.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2022-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84184134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monitoring reliability under competing risks using field data","authors":"F. Pascual, Joseph P. Navelski","doi":"10.1080/00224065.2022.2080617","DOIUrl":"https://doi.org/10.1080/00224065.2022.2080617","url":null,"abstract":"Abstract Many modern products fail due to one of multiple causes called competing risks. In this article, we propose variable features for monitoring product failure by control charts under competing risks. Failure reports arrive one at a time from a sample of population of units. Features are derived from both the reports and the assumed competing-risk statistical model. To assess the efficacy of different feature subsets in detecting shifts in the failure-time process, we consider control charts based on random forests and compare the average run length performances under different shift scenarios. We demonstrate the control charts with both simulated data sets and actual field data set from a consulting problem. We also propose graphical fault-diagnosis methods for identifying assignable causes of alarm signals. Control charts based on the proposed features will provide valuable information to manufacturers in planning for warranty, part-replacement, or repair.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74895174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and Analysis of Experiments and Observational Studies using R","authors":"Joseph D. Conklin","doi":"10.1080/00224065.2022.2096515","DOIUrl":"https://doi.org/10.1080/00224065.2022.2096515","url":null,"abstract":"","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2022-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85600900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-level transfer learning for degradation modeling and prognosis","authors":"Amirhossein Fallahdizcheh, Chao Wang","doi":"10.1080/00224065.2022.2081103","DOIUrl":"https://doi.org/10.1080/00224065.2022.2081103","url":null,"abstract":"Abstract The typical way to conduct data-driven prognosis is to train a degradation model with historical data, then apply the model to predict failure for in-service units. Most existing works assume the historical data and in-service data are from the same process. In practice, however, different but related processes can share similar degradation patterns. Thus, the historical data from these processes are expected to provide useful prognosis information for each other. In this article, we propose a data-level transfer learning framework to extract useful and shared information from different processes to benefit the prognosis of in-service units. In this framework, the degradation data in each process is modeled by a mixed effects model. To facilitate the information sharing among different mixed effects models, a hierarchical Bayesian structure is proposed to model and connect the distributions of mixed effects in different mixed models. Because the degradation paths in different processes are rarely the same, the dimension of the mixed effects/regressor in each process can be different. To handle this issue, we propose a tailored linear transformation to marginalize or expand the distributions of mixed effects in different degradation processes to achieve consistent dimensions. The transferred information is finally incorporated with the degradation data from in-service units to conduct prognosis. The proposed method is validated and compared with various benchmarks in extensive numerical studies and two case studies. The results show the proposed method can successfully transfer useful information in different processes to benefit the prognosis.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81621824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatio-temporal process monitoring using exponentially weighted spatial LASSO","authors":"Peihua Qiu, Kai-zuan Yang","doi":"10.1080/00224065.2022.2081104","DOIUrl":"https://doi.org/10.1080/00224065.2022.2081104","url":null,"abstract":"Abstract Spatio-temporal process monitoring (STPM) has received a considerable attention recently due to its broad applications in environment monitoring, disease surveillance, streaming image processing, and more. Because spatio-temporal data often have complicated structure, including latent spatio-temporal data correlation, complex spatio-temporal mean structure, and nonparametric data distribution, STPM is a challenging research problem. In practice, if a spatio-temporal process has a distributional shift (e.g., mean shift) started at a specific time point, then the spatial locations with the shift are usually clustered in small regions. This kind of spatial feature of the shift has not been considered in the existing STPM literature yet. In this paper, we develop a new STPM method that takes into account the spatial feature of the shift in its construction. The new method combines the ideas of exponentially weighted moving average in the temporal domain for online process monitoring and spatial LASSO in the spatial domain for accommodating the spatial feature of a future shift. It can also accommodate the complicated spatio-temporal data structure well. Both simulation studies and a real-data application show that it can provide a reliable and effective tool for different STPM applications.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2022-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74938549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Book review: Introduction to statistical process control","authors":"W. Woodall","doi":"10.1080/00224065.2022.2060150","DOIUrl":"https://doi.org/10.1080/00224065.2022.2060150","url":null,"abstract":"","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2022-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84374739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive toolbox for the gamma distribution: The gammadist package","authors":"Piao Chen, Kilian Buis, Xiujie Zhao","doi":"10.1080/00224065.2022.2053794","DOIUrl":"https://doi.org/10.1080/00224065.2022.2053794","url":null,"abstract":"Abstract The gamma distribution is one of the most important parametric models in probability theory and statistics. Although a multitude of studies have theoretically investigated the properties of the gamma distribution in the literature, there is still a serious lack of tailored statistical tools to facilitate its practical applications. To fill the gap, this paper develops a comprehensive R package for the gamma distribution. In specific, the R package focuses on the following three important tasks: generate the gamma random variables, estimate the model parameters, and construct statistical limits, including confidence limits, prediction limits, and tolerance limits based on the gamma random variables. The proposed package encompasses the state-of-the-art methods of the gamma distribution in the literature and its usage is illustrated by a real application.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2022-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80021599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Introduction to High-Dimensional Statistics, Christophe Giraud. Chapman& Hall/CRC Press, 2021, 364 pp., $72.00 hardcover, ISBN 978-0-367-71622-6.","authors":"Caleb King","doi":"10.1080/00224065.2022.2041378","DOIUrl":"https://doi.org/10.1080/00224065.2022.2041378","url":null,"abstract":"A deep dive into the theoretical underpinnings of common high-dimensional statistical 20 techniques, Dr. Giraud’s Introduction to High-Dimensional Statistics is a good reference for those who wish to explore the mathematical foundations of state-of-the-art multivariate methods. The book covers a wide array of topics, from estimation bounds to multivariate regression and even clustering. In this 2nd edition, Dr. Giraud expands his work to include more recent advances and statistical methods. The book consists of 12 chapters, starting with a brief introduction to the complexities of conducting statistics in high dimensions. The book then proceeds similar to a standard statistical textbook, moving from properties of statistical estimators to statistical modeling, including regression and then other more advanced topics. Each chapter concludes with a set of exercises, many of which are portions of proofs from the chapter left for the reader. All that being said, do not let the title of the book fool you. By the author’s own admission, this is not an introduction on the same level as Hastie et al.’s Elements of Statistical Learning. Instead, the focus of this book is on the mathematical foundations of high-dimensional techniques, proving theorems regarding properties of estimators. I must confess this is not quite what I expected upon first look; one truly cannot judge a book by its cover. That is not to say that this book is lacking. It is impressive in its efficient, yet thorough, presentation of the theory. I especially appreciated how the author took time at the beginning to illustrate some of the strange behavior one encounters in very high dimensions. However, I did find it jarring that mathematical notation was very often used without much introduction. There is an appendix with notations at the end of the book, but I would’ve rather had a bit more interpretation within the text rather than having to flip back and forth. There were also a few typographical errors and partial omissions of formulas, though I can’t be sure if this was part of the text or bugs in the software I used to read the digital version. In summary, this book would certainly make for a good graduate level textbook in an advanced course on statistical methods. If you are willing to put the necessary time and investment into rigorously exploring the foundations of high-dimensional statistics, than you can hardly do better than this book.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2022-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90561275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian Modeling and Computation in Python","authors":"Shuai Huang","doi":"10.1080/00224065.2022.2041379","DOIUrl":"https://doi.org/10.1080/00224065.2022.2041379","url":null,"abstract":"This book is useful for readers who want to hone their skills in Bayesian modeling and computation. Written by experts in the area of Bayesian software and major contributors to some existing widely used Bayesian computational tools, this book covers not only basic Bayesian probabilistic inference but also a range of models from linear models (and mixed effect models, hierarchical models, splines, etc) to time series models such as the state space model. It also covers the Bayesian additive regression trees. Almost all the concepts and techniques are implemented using PyMC3, Tensorflow Probability (TFP), ArviZ and other libraries. By doing all the modeling, computation, and data analysis, the authors not only show how these things work, but also show how and why things don’t work by emphasis on exploratory data analysis, model comparison, and diagnostics. To learn from the book, readers may need some statistical background such as basic training in statistics and probability theory. Some understanding of Bayesian modeling and inference is also needed, such as the concepts of prior, likelihood, posterior, the bayes’s law, and Monte Carlo sampling. Some experience with Python would also be very beneficial for readers to get started on this journey of Bayesian modeling. The authors suggested a few books as possible preliminaries for their book. I feel that the readers may also benefit from reading Andrew Gelman’s book, Bayesian Data Analysis, Chapman & Hall/CRC, 3rd Edition, 2013. Of course, as the authors pointed it out, this book is not for a Bayesian Reader but a Bayesian practitioner. The book is more of an interactive experience for Bayesian practitioners by learning all the computational tools to model and to negotiate with data for a good modeling practice. On the other hand, if readers have already had experience with real-world data analysis using Python or R or other similar tools, even if this book is their first experience with Bayesian modeling and computation, readers may still learn a lot from this book. There are an abundance of figures and detailed explanations of how things are done and how the results are interpreted. Picking up these details would need some trained sensibility when dealing with real-world data, but aspiring and experienced practitioners should find all the details useful and impressive. And there are also many big picture schematic drawings to help readers connect all the details with overall concepts such as end-to-end workflows. The Figure 9.1 is a remarkable example. Overall, as Kevin Murphy pointed out in the Forward, “this is a valuable addition to the literature, which should hopefully further the adoption of Bayesian methods”. I highly recommend readers who are interested in learning Bayesian models and their applications in practice to have this book on their bookshelf.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78872910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"cpss: an package for change-point detection by sample-splitting methods","authors":"Guanghui Wang, Changliang Zou","doi":"10.1080/00224065.2022.2035284","DOIUrl":"https://doi.org/10.1080/00224065.2022.2035284","url":null,"abstract":"Abstract Change-point detection is a popular statistical method for Phase I analysis in statistical process control. The cpss package has been developed to provide users with multiple choices of change-point searching algorithms for a variety of frequently considered parametric change-point models, including the univariate and multivariate mean and/or (co)variance change models, changes in linear models and generalized linear models, and change models in exponential families. In particular, it integrates the recently proposed COPSS criterion to determine the number of change-points in a data-driven fashion that avoids selecting or specifying additional tuning parameters in existing approaches. Hence it is more convenient to use in practical applications. In addition, the cpss package brings great possibilities to handle user-customized change-point models.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85866418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}