TechnometricsPub Date : 2025-01-01Epub Date: 2025-01-30DOI: 10.1080/00401706.2024.2441679
Di Wang, Xiaochen Xian, Haidong Li
{"title":"Spatiotemporal Interactive Modeling of Event-based Dynamic Networks.","authors":"Di Wang, Xiaochen Xian, Haidong Li","doi":"10.1080/00401706.2024.2441679","DOIUrl":"10.1080/00401706.2024.2441679","url":null,"abstract":"<p><p>Event-based dynamic networks exist in a wide range of areas, including traffic, biological, and social applications. Such a network consists of interaction event sequences over different locations, where each event may trigger or influence a series of subsequent events under certain intrinsic spatial structure because of their geographical and semantic proximities. Such influence patterns and triggering motivations reflect the nature and semantics of human/object behaviors in the network. Thus, modeling event-based dynamic networks properly is critically important. This paper proposes a spatiotemporal interactive Hawkes process (SIHP) that describes how a series of events occurs and models the rate of interaction events between any pair of nodes on the network explicitly with the information from related historical events as well as geographical and semantic neighboring nodes. The proposed SIHP can not only learn the patterns of influence from historical interaction events on later ones, but can also understand the network dynamics by fully considering spatial structure knowledge. Specifically, we incorporate prior knowledge of spatial structure as a graph and design graph regularization in the SIHP, where model parameters are estimated by designing an alternating direction method of multiplier (ADMM) framework. Numerical experiments and a real case study on New York yellow taxi data validate the effectiveness of the proposed method.</p>","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":"67 2","pages":"293-310"},"PeriodicalIF":2.5,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12711312/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145782826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
TechnometricsPub Date : 2025-01-01Epub Date: 2025-02-03DOI: 10.1080/00401706.2024.2444310
Youngdeok Hwang, Hang J Kim, Won Chang, Christian Hong, Steven N MacEachern
{"title":"Bayesian Model Calibration and Sensitivity Analysis for Oscillating Biological Experiments.","authors":"Youngdeok Hwang, Hang J Kim, Won Chang, Christian Hong, Steven N MacEachern","doi":"10.1080/00401706.2024.2444310","DOIUrl":"10.1080/00401706.2024.2444310","url":null,"abstract":"<p><p>Understanding the oscillating behaviors that govern organisms' internal biological processes requires interdisciplinary efforts combining both biological and computer experiments, as the latter can complement the former by simulating perturbed conditions with higher resolution. Harmonizing the two types of experiment, however, poses significant statistical challenges due to identifiability issues, numerical instability, and ill behavior in high dimension. This article devises a new Bayesian calibration framework for oscillating biochemical models. The proposed Bayesian model is estimated relying on an advanced Markov chain Monte Carlo (MCMC) technique which can efficiently infer the parameter values that match the simulated and observed oscillatory processes. Also proposed is an approach to sensitivity analysis based on the intervention posterior. This approach measures the influence of individual parameters on the target process by using the obtained MCMC samples as a computational tool. The proposed framework is illustrated with circadian oscillations observed in a filamentous fungus, <i>Neurospora crassa</i>.</p>","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":"67 2","pages":"333-343"},"PeriodicalIF":2.5,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12720118/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145820718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
TechnometricsPub Date : 2025-01-01Epub Date: 2024-12-23DOI: 10.1080/00401706.2024.2430204
James Yang, Trevor Hastie
{"title":"Note on the Equivalence of Orthogonalizing EM and Proximal Gradient Descent.","authors":"James Yang, Trevor Hastie","doi":"10.1080/00401706.2024.2430204","DOIUrl":"10.1080/00401706.2024.2430204","url":null,"abstract":"<p><p>Xiong et al. (2016) develop a method called orthogonalizing EM (OEM) to solve penalized regression problems for tall data. While OEM is developed in the context of the EM algorithm, we show that it is, in fact, an instance of proximal gradient descent, a popular first-order convex optimization algorithm.</p>","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":"67 2","pages":"267-269"},"PeriodicalIF":2.5,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12140180/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144249688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
TechnometricsPub Date : 2024-08-23DOI: 10.1080/00401706.2024.2394475
Romain Ait Abdelmalek-Lomenech, Julien Bect, Vincent Chabridon, Emmanuel Vazquez
{"title":"Bayesian sequential design of computer experiments for quantile set inversion","authors":"Romain Ait Abdelmalek-Lomenech, Julien Bect, Vincent Chabridon, Emmanuel Vazquez","doi":"10.1080/00401706.2024.2394475","DOIUrl":"https://doi.org/10.1080/00401706.2024.2394475","url":null,"abstract":"We consider an unknown multivariate function representing a system—such as a complex numerical simulator—taking both deterministic and uncertain inputs. Our objective is to estimate the set of dete...","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":"8 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
TechnometricsPub Date : 2024-08-07DOI: 10.1080/00401706.2024.2374189
Stan Lipovetsky
{"title":"The Planetary Atom: A Fictional Account of George Adolphus Schott, the Forgotten Physicist","authors":"Stan Lipovetsky","doi":"10.1080/00401706.2024.2374189","DOIUrl":"https://doi.org/10.1080/00401706.2024.2374189","url":null,"abstract":"Published in Technometrics (Vol. 66, No. 3, 2024)","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":"122 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141931283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
TechnometricsPub Date : 2024-08-07DOI: 10.1080/00401706.2024.2374190
Egi Rahmansyah, Nur Hidayah, Megawati Zein Waliulu, Hawinda Restu Putri
{"title":"Data Science and Machine Learning for Non-Programmers Using SAS Enterprise Miner, 1st ed.","authors":"Egi Rahmansyah, Nur Hidayah, Megawati Zein Waliulu, Hawinda Restu Putri","doi":"10.1080/00401706.2024.2374190","DOIUrl":"https://doi.org/10.1080/00401706.2024.2374190","url":null,"abstract":"Published in Technometrics (Vol. 66, No. 3, 2024)","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":"22 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141931284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
TechnometricsPub Date : 2024-08-07DOI: 10.1080/00401706.2024.2374184
Sukardi, Puji Lestari
{"title":"Statistical Inference Based on Kernel Distribution Function Estimators","authors":"Sukardi, Puji Lestari","doi":"10.1080/00401706.2024.2374184","DOIUrl":"https://doi.org/10.1080/00401706.2024.2374184","url":null,"abstract":"Published in Technometrics (Vol. 66, No. 3, 2024)","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":"29 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141931281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
TechnometricsPub Date : 2024-08-07DOI: 10.1080/00401706.2024.2374188
Zulfaidil, La Ode Muhamad Iqbal, Riani Utami, Sri Redjeki Pudjaprasetya, Warsoma Djohan
{"title":"Molecular Networking Statistical Mechanics in the Age of AI and Machine Learning","authors":"Zulfaidil, La Ode Muhamad Iqbal, Riani Utami, Sri Redjeki Pudjaprasetya, Warsoma Djohan","doi":"10.1080/00401706.2024.2374188","DOIUrl":"https://doi.org/10.1080/00401706.2024.2374188","url":null,"abstract":"Published in Technometrics (Vol. 66, No. 3, 2024)","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":"95 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141968653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}