Pub Date : 2024-07-23DOI: 10.1080/00031305.2024.2368803
Alan R. Vazquez, Xiaocong Xuan
{"title":"A review of Design of Experiments courses offered to undergraduate students at American universities","authors":"Alan R. Vazquez, Xiaocong Xuan","doi":"10.1080/00031305.2024.2368803","DOIUrl":"https://doi.org/10.1080/00031305.2024.2368803","url":null,"abstract":"","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"139 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141811012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-20DOI: 10.1080/00031305.2024.2356095
Anthony Zeimbekakis, Elizabeth D. Schifano, Jun Yan
{"title":"On Misuses of the Kolmogorov–Smirnov Test for One-Sample Goodness-of-Fit","authors":"Anthony Zeimbekakis, Elizabeth D. Schifano, Jun Yan","doi":"10.1080/00031305.2024.2356095","DOIUrl":"https://doi.org/10.1080/00031305.2024.2356095","url":null,"abstract":"","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"4 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141119399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-05DOI: 10.1080/00031305.2024.2314480
Caroline M Kerfonta, Sunuk Kim, Ye Chen, Qiong Zhang, Mo Jiang
{"title":"Sequential Selection for Minimizing the Variance with Application to Crystallization Experiments","authors":"Caroline M Kerfonta, Sunuk Kim, Ye Chen, Qiong Zhang, Mo Jiang","doi":"10.1080/00031305.2024.2314480","DOIUrl":"https://doi.org/10.1080/00031305.2024.2314480","url":null,"abstract":"","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"19 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139802477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-05DOI: 10.1080/00031305.2024.2314480
Caroline M Kerfonta, Sunuk Kim, Ye Chen, Qiong Zhang, Mo Jiang
{"title":"Sequential Selection for Minimizing the Variance with Application to Crystallization Experiments","authors":"Caroline M Kerfonta, Sunuk Kim, Ye Chen, Qiong Zhang, Mo Jiang","doi":"10.1080/00031305.2024.2314480","DOIUrl":"https://doi.org/10.1080/00031305.2024.2314480","url":null,"abstract":"","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"237 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139862382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-02DOI: 10.1080/00031305.2024.2304534
{"title":"The American Statistician 2023 Associate Editors","authors":"","doi":"10.1080/00031305.2024.2304534","DOIUrl":"https://doi.org/10.1080/00031305.2024.2304534","url":null,"abstract":"","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"116 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140515986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-13DOI: 10.1080/00031305.2023.2282629
Marissa C. Ashner, Tanya P. Garcia
AbstractDespite its drawbacks, the complete case analysis is commonly used in regression models with incomplete covariates. Understanding when the complete case analysis will lead to consistent parameter estimation is vital before use. Our aim here is to demonstrate when a complete case analysis is consistent for randomly right-censored covariates and to discuss the implications of its use even when consistent. Across the censored covariate literature, different assumptions are made to ensure a complete case analysis produces a consistent estimator, which leads to confusion in practice. We make several contributions to dispel this confusion. First, we summarize the language surrounding the assumptions that lead to a consistent complete case estimator. Then, we show a unidirectional hierarchical relationship between these assumptions, which leads us to one sufficient assumption to consider before using a complete case analysis. Lastly, we conduct a simulation study to illustrate the performance of a complete case analysis with a right-censored covariate under different censoring mechanism assumptions, and we demonstrate its use with a Huntington disease data example.Keywords: censoring mechanism assumptionscomplete case analysisrandomly censored covariatesDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.
{"title":"Understanding the implications of a complete case analysis for regression models with a right-censored covariate","authors":"Marissa C. Ashner, Tanya P. Garcia","doi":"10.1080/00031305.2023.2282629","DOIUrl":"https://doi.org/10.1080/00031305.2023.2282629","url":null,"abstract":"AbstractDespite its drawbacks, the complete case analysis is commonly used in regression models with incomplete covariates. Understanding when the complete case analysis will lead to consistent parameter estimation is vital before use. Our aim here is to demonstrate when a complete case analysis is consistent for randomly right-censored covariates and to discuss the implications of its use even when consistent. Across the censored covariate literature, different assumptions are made to ensure a complete case analysis produces a consistent estimator, which leads to confusion in practice. We make several contributions to dispel this confusion. First, we summarize the language surrounding the assumptions that lead to a consistent complete case estimator. Then, we show a unidirectional hierarchical relationship between these assumptions, which leads us to one sufficient assumption to consider before using a complete case analysis. Lastly, we conduct a simulation study to illustrate the performance of a complete case analysis with a right-censored covariate under different censoring mechanism assumptions, and we demonstrate its use with a Huntington disease data example.Keywords: censoring mechanism assumptionscomplete case analysisrandomly censored covariatesDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"49 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136347423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-10DOI: 10.1080/00031305.2023.2282631
Jessica Allen, Ting Wang
AbstractLow-frequency earthquakes (LFEs) are small magnitude earthquakes with frequencies of 1-10 Hertz which often occur in overlapping sequence forming persistent seismic tremors. They provide insights into large earthquake processes along plate boundaries. LFEs occur stochastically in time, often forming temporally recurring clusters. The occurrence times are typically modeled using point processes and their intensity functions. We demonstrate how to use hidden Markov models coupled with visualization techniques to model inter-arrival times directly, classify LFE occurrence patterns along the San Andreas Fault, and perform model selection. We highlight two subsystems of LFE activity corresponding to periods of alternating episodic and quiescent behavior.Keywords: Classificationmodel selectionSan Andreas FaultvisualizationViterbi pathDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. AcknowledgmentsThe authors wish to thank Jodie Buckby for sharing code to fit and optimize hidden Markov models, and providing general assistance. We are grateful to Marco Brenna for advice regarding geological interpretations, visualization, and additional edits. The authors also wish to acknowledge the use of New Zealand eScience Infrastructure (NeSI) high performance computing facilities and consulting support as part of this research. New Zealand’s national facilities are provided by NeSI and funded jointly by NeSI’s collaborator institutions and through the Ministry of Business, Innovation & Employment’s Research Infrastructure program. URL https://www.nesi.org.nz. The authors acknowledge the suggestions of two anonymous reviewers in enhancing this paper.Supplementary MaterialsThe Supplementary Material contains (i) R code used for the analysis, (ii) tables of results for gamma, Weibull, log-normal and normal hidden Markov models in Section 4.1, (iii) additional pseudo-residual plots and sojourn checks for Section 4.1, (iv) additional probability density plots and plot of state splitting sequence for Section 4.3, (v) Viterbi path visualizations for the entire observation period and years 2005, 2006 and 2007 for Section 4.4, (vi) sojourn checks for subsystems in Section 4.5.Disclosure StatementThe authors report that there are no competing interests to declare.
{"title":"Hidden Markov Models for Low-Frequency Earthquake Recurrence","authors":"Jessica Allen, Ting Wang","doi":"10.1080/00031305.2023.2282631","DOIUrl":"https://doi.org/10.1080/00031305.2023.2282631","url":null,"abstract":"AbstractLow-frequency earthquakes (LFEs) are small magnitude earthquakes with frequencies of 1-10 Hertz which often occur in overlapping sequence forming persistent seismic tremors. They provide insights into large earthquake processes along plate boundaries. LFEs occur stochastically in time, often forming temporally recurring clusters. The occurrence times are typically modeled using point processes and their intensity functions. We demonstrate how to use hidden Markov models coupled with visualization techniques to model inter-arrival times directly, classify LFE occurrence patterns along the San Andreas Fault, and perform model selection. We highlight two subsystems of LFE activity corresponding to periods of alternating episodic and quiescent behavior.Keywords: Classificationmodel selectionSan Andreas FaultvisualizationViterbi pathDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. AcknowledgmentsThe authors wish to thank Jodie Buckby for sharing code to fit and optimize hidden Markov models, and providing general assistance. We are grateful to Marco Brenna for advice regarding geological interpretations, visualization, and additional edits. The authors also wish to acknowledge the use of New Zealand eScience Infrastructure (NeSI) high performance computing facilities and consulting support as part of this research. New Zealand’s national facilities are provided by NeSI and funded jointly by NeSI’s collaborator institutions and through the Ministry of Business, Innovation & Employment’s Research Infrastructure program. URL https://www.nesi.org.nz. The authors acknowledge the suggestions of two anonymous reviewers in enhancing this paper.Supplementary MaterialsThe Supplementary Material contains (i) R code used for the analysis, (ii) tables of results for gamma, Weibull, log-normal and normal hidden Markov models in Section 4.1, (iii) additional pseudo-residual plots and sojourn checks for Section 4.1, (iv) additional probability density plots and plot of state splitting sequence for Section 4.3, (v) Viterbi path visualizations for the entire observation period and years 2005, 2006 and 2007 for Section 4.4, (vi) sojourn checks for subsystems in Section 4.5.Disclosure StatementThe authors report that there are no competing interests to declare.","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"73 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135093451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-08DOI: 10.1080/00031305.2023.2281359
David B. Hitchcock
Abstract–A special-topics undergraduate course about the history of statistics which was taught in Spring 2023 at the University of South Carolina is described. We review other similar courses (past and current) and explain the discussion-based nature of this course. The conception and planning of the course are detailed, and the unique experiences (activities, guest speakers, presentations, etc.) are described. The course emphasized substantial amounts of independent reading outside of class and lively discussions during class. Topics covered in the class include the early development of probability, the normal distribution, and the central limit theorem; the development of modern statistical science by British statisticians; the rise of formal mathematical statistics; and increasing specialization and modern computational and data-analytic advances. An assessment of the course’s effectiveness based on qualitative student survey data is given. Students were highly complimentary of the course, praising the in-class discussion format, the benefits of doing the outside readings, the invited guest speakers, and the in-class activities. There were occasional comments that the amount of required reading was excessive. Based on this, suggestions for future offerings of the course are presented, including developing a more carefully curated set of readings.KEY WORDS: Teachingpedagogyroundtable discussionsundergraduate coursehistorical contenthonors courseDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.
{"title":"Lessons from a Discussion-based Course on the History of Statistics","authors":"David B. Hitchcock","doi":"10.1080/00031305.2023.2281359","DOIUrl":"https://doi.org/10.1080/00031305.2023.2281359","url":null,"abstract":"Abstract–A special-topics undergraduate course about the history of statistics which was taught in Spring 2023 at the University of South Carolina is described. We review other similar courses (past and current) and explain the discussion-based nature of this course. The conception and planning of the course are detailed, and the unique experiences (activities, guest speakers, presentations, etc.) are described. The course emphasized substantial amounts of independent reading outside of class and lively discussions during class. Topics covered in the class include the early development of probability, the normal distribution, and the central limit theorem; the development of modern statistical science by British statisticians; the rise of formal mathematical statistics; and increasing specialization and modern computational and data-analytic advances. An assessment of the course’s effectiveness based on qualitative student survey data is given. Students were highly complimentary of the course, praising the in-class discussion format, the benefits of doing the outside readings, the invited guest speakers, and the in-class activities. There were occasional comments that the amount of required reading was excessive. Based on this, suggestions for future offerings of the course are presented, including developing a more carefully curated set of readings.KEY WORDS: Teachingpedagogyroundtable discussionsundergraduate coursehistorical contenthonors courseDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"17 2‐3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135342012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-30DOI: 10.1080/00031305.2023.2277156
Ronald Christensen
{"title":"Comment on “Forbidden knowledge and specialized training: A versatile solution for the two main sources of overfitting in linear regression,” by Rohlfs (2023)","authors":"Ronald Christensen","doi":"10.1080/00031305.2023.2277156","DOIUrl":"https://doi.org/10.1080/00031305.2023.2277156","url":null,"abstract":"","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136103960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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