Pub Date : 2024-08-05DOI: 10.1080/00401706.2024.2388547
Lei Jin, Bo Li
Statistical comparison of multiple time series in their underlying frequency patterns has many real applications. However, existing methods are only applicable to a small number of mutually indepen...
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Pub Date : 2024-07-08DOI: 10.1080/00401706.2024.2376173
Junoh Heo, Chih-Li Sung
Computer simulations have become essential for analyzing complex systems, but high-fidelity simulations often come with significant computational costs. To tackle this challenge, multi-fidelity com...
{"title":"Active Learning for a Recursive Non-Additive Emulator for Multi-Fidelity Computer Experiments","authors":"Junoh Heo, Chih-Li Sung","doi":"10.1080/00401706.2024.2376173","DOIUrl":"https://doi.org/10.1080/00401706.2024.2376173","url":null,"abstract":"Computer simulations have become essential for analyzing complex systems, but high-fidelity simulations often come with significant computational costs. To tackle this challenge, multi-fidelity com...","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":"20 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141576451","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}
Pub Date : 2024-07-02DOI: 10.1080/00401706.2024.2374554
Henrik Imberg, Xiaomi Yang, Carol Flannagan, Jonas Bärgman
Data subsampling has become widely recognized as a tool to overcome computational and economic bottlenecks in analyzing massive datasets. We contribute to the development of adaptive design for est...
{"title":"Active sampling: A machine-learning-assisted framework for finite population inference with optimal subsamples","authors":"Henrik Imberg, Xiaomi Yang, Carol Flannagan, Jonas Bärgman","doi":"10.1080/00401706.2024.2374554","DOIUrl":"https://doi.org/10.1080/00401706.2024.2374554","url":null,"abstract":"Data subsampling has become widely recognized as a tool to overcome computational and economic bottlenecks in analyzing massive datasets. We contribute to the development of adaptive design for est...","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":"38 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141552268","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}
Pub Date : 2024-06-11DOI: 10.1080/00401706.2024.2365730
Haoxuan Wu, Toryn L. J. Schafer, Sean Ryan, David S. Matteson
We introduce a new approach for decoupling trends (drift) and changepoints (shifts) in time series. Our locally adaptive model-based approach for robustly decoupling combines Bayesian trend filteri...
{"title":"Drift vs Shift: Decoupling Trends and Changepoint Analysis","authors":"Haoxuan Wu, Toryn L. J. Schafer, Sean Ryan, David S. Matteson","doi":"10.1080/00401706.2024.2365730","DOIUrl":"https://doi.org/10.1080/00401706.2024.2365730","url":null,"abstract":"We introduce a new approach for decoupling trends (drift) and changepoints (shifts) in time series. Our locally adaptive model-based approach for robustly decoupling combines Bayesian trend filteri...","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":"105 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141508999","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}
Pub Date : 2024-05-07DOI: 10.1080/00401706.2024.2336542
Jeremy Oguamalam, Una Radojičić, Peter Filzmoser
We propose the Minimum Regularized Covariance Trace (MRCT) estimator, a novel method for robust covariance estimation and functional outlier detection designed primarily for dense functional data. ...
{"title":"Minimum Regularized Covariance Trace Estimator and Outlier Detection for Functional Data","authors":"Jeremy Oguamalam, Una Radojičić, Peter Filzmoser","doi":"10.1080/00401706.2024.2336542","DOIUrl":"https://doi.org/10.1080/00401706.2024.2336542","url":null,"abstract":"We propose the Minimum Regularized Covariance Trace (MRCT) estimator, a novel method for robust covariance estimation and functional outlier detection designed primarily for dense functional data. ...","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":"115 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140936655","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}
Pub Date : 2024-05-03DOI: 10.1080/00401706.2024.2350436
Ying Sheng, Jing Qin, Chiung-Yu Huang
With the rapidly increasing availability of large-scale and high-velocity streaming data, efficient algorithms that can process data in batches without requiring expensive storage and computation r...
{"title":"Sequential Data Integration Under Dataset Shift","authors":"Ying Sheng, Jing Qin, Chiung-Yu Huang","doi":"10.1080/00401706.2024.2350436","DOIUrl":"https://doi.org/10.1080/00401706.2024.2350436","url":null,"abstract":"With the rapidly increasing availability of large-scale and high-velocity streaming data, efficient algorithms that can process data in batches without requiring expensive storage and computation r...","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":"21 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140936840","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}
Pub Date : 2024-05-03DOI: 10.1080/00401706.2024.2350421
Amandine Pierrot, Pierre Pinson
We consider a new framework where a continuous, though bounded, random variable has unobserved bounds that vary over time. In the context of univariate time series, we look at the bounds as paramet...
{"title":"On tracking varying bounds when forecasting bounded time series","authors":"Amandine Pierrot, Pierre Pinson","doi":"10.1080/00401706.2024.2350421","DOIUrl":"https://doi.org/10.1080/00401706.2024.2350421","url":null,"abstract":"We consider a new framework where a continuous, though bounded, random variable has unobserved bounds that vary over time. In the context of univariate time series, we look at the bounds as paramet...","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":"41 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140936800","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}
Pub Date : 2024-05-01DOI: 10.1080/00401706.2024.2339789
Firdous Ahmad Mala, Snowbar Majeed
Published in Technometrics (Vol. 66, No. 2, 2024)
发表于《技术计量学》（第 66 卷第 2 期，2024 年）
{"title":"Hands-On Data Science for Librarians","authors":"Firdous Ahmad Mala, Snowbar Majeed","doi":"10.1080/00401706.2024.2339789","DOIUrl":"https://doi.org/10.1080/00401706.2024.2339789","url":null,"abstract":"Published in Technometrics (Vol. 66, No. 2, 2024)","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":"11 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140833074","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}
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