Pub Date : 2021-04-03DOI: 10.1080/09332480.2021.1915043
Amanda Peterson-Plunkett
{"title":"Editor's Letter","authors":"Amanda Peterson-Plunkett","doi":"10.1080/09332480.2021.1915043","DOIUrl":"https://doi.org/10.1080/09332480.2021.1915043","url":null,"abstract":"","PeriodicalId":88226,"journal":{"name":"Chance (New York, N.Y.)","volume":"21 1","pages":"3 - 3"},"PeriodicalIF":0.0,"publicationDate":"2021-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83338136","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 : 2021-04-03DOI: 10.1080/09332480.2021.1915031
Chase Marchand, Dalton Maahs
Marchand and Maahs discuss Benford's Law and COVID-19 data Benford's law was first discovered by Simon Newcomb in his 1881 work in the American Journal of Mathematics When Newcomb used logarithm tables, he noticed how much faster the first pages in the bound tables were wearing out than the last pages Newcomb quantified his observation with a logarithmic law that gives the probability of occurrence for the first significant digit--now known as Benford's law Data sets for COVID-19 tend to be a very good fit for Benford's law They include several categories of daily data from both the US and the world These findings also open up the possibility of Benford's law being used in the future to assess the accuracy of reported sets of COVID-19 data
{"title":"Benford's Law and COVID-19 Data","authors":"Chase Marchand, Dalton Maahs","doi":"10.1080/09332480.2021.1915031","DOIUrl":"https://doi.org/10.1080/09332480.2021.1915031","url":null,"abstract":"Marchand and Maahs discuss Benford's Law and COVID-19 data Benford's law was first discovered by Simon Newcomb in his 1881 work in the American Journal of Mathematics When Newcomb used logarithm tables, he noticed how much faster the first pages in the bound tables were wearing out than the last pages Newcomb quantified his observation with a logarithmic law that gives the probability of occurrence for the first significant digit--now known as Benford's law Data sets for COVID-19 tend to be a very good fit for Benford's law They include several categories of daily data from both the US and the world These findings also open up the possibility of Benford's law being used in the future to assess the accuracy of reported sets of COVID-19 data","PeriodicalId":88226,"journal":{"name":"Chance (New York, N.Y.)","volume":"43 1","pages":"31 - 38"},"PeriodicalIF":0.0,"publicationDate":"2021-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90541522","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 : 2021-04-03DOI: 10.1080/09332480.2021.1915040
H. Wainer, R. Feinberg
This investigation was instigated by the horrific events of October 27, 2018, when Joyce Fienberg, an old and dear friend, was one of 11 congregants murdered in Pittsburgh’s Tree of Life synagogue (Pittsburgh Post-Gazette, 2018); Joyce was the widow of famed statistician (and founding editor of CHANCE) Stephen E. Fienberg. As time passed, we grew numb, but the spirit of Tikkun Olam kindled the desire to try to do something. But what? We are statisticians (and Jews), so our path to understanding and thus perhaps diminishing the likelihood of the recurrence of such terrible events begins with looking at data. is account is the start of our empirical search for understanding. e path to understanding is paved with questions whose answers provide illumination. Although many of these questions are causal, we must begin with some obvious questions whose answers are descriptive. ree categories of questions that require descriptive answers are:
{"title":"Looking at Reported Hate Crimes","authors":"H. Wainer, R. Feinberg","doi":"10.1080/09332480.2021.1915040","DOIUrl":"https://doi.org/10.1080/09332480.2021.1915040","url":null,"abstract":"This investigation was instigated by the horrific events of October 27, 2018, when Joyce Fienberg, an old and dear friend, was one of 11 congregants murdered in Pittsburgh’s Tree of Life synagogue (Pittsburgh Post-Gazette, 2018); Joyce was the widow of famed statistician (and founding editor of CHANCE) Stephen E. Fienberg. As time passed, we grew numb, but the spirit of Tikkun Olam kindled the desire to try to do something. But what? We are statisticians (and Jews), so our path to understanding and thus perhaps diminishing the likelihood of the recurrence of such terrible events begins with looking at data. is account is the start of our empirical search for understanding. e path to understanding is paved with questions whose answers provide illumination. Although many of these questions are causal, we must begin with some obvious questions whose answers are descriptive. ree categories of questions that require descriptive answers are:","PeriodicalId":88226,"journal":{"name":"Chance (New York, N.Y.)","volume":"27 1","pages":"65 - 72"},"PeriodicalIF":0.0,"publicationDate":"2021-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90218081","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 : 2021-04-03DOI: 10.1080/09332480.2021.1915028
J. T., N. B., T. Middleton
Nicholas and Middleton discuss the economic and societal impact of non-pharmaceutical interventions (NPI) during the COVID-19 pandemic Like many countries affected by the COVID-19 pandemic, the UK has deployed a series of national and local NPIs in an effort to control the spread of the virus While others have analyzed the epidemiological impact, on cases and hospitalizations, for example, the broader economic and societal impact can be considered by asking whether, in simple terms, light can be shed on the cost of these measures
{"title":"Modeling the Economic and Societal Impact of Non‐Pharmaceutical Interventions During the COVID‐19 Pandemic","authors":"J. T., N. B., T. Middleton","doi":"10.1080/09332480.2021.1915028","DOIUrl":"https://doi.org/10.1080/09332480.2021.1915028","url":null,"abstract":"Nicholas and Middleton discuss the economic and societal impact of non-pharmaceutical interventions (NPI) during the COVID-19 pandemic Like many countries affected by the COVID-19 pandemic, the UK has deployed a series of national and local NPIs in an effort to control the spread of the virus While others have analyzed the epidemiological impact, on cases and hospitalizations, for example, the broader economic and societal impact can be considered by asking whether, in simple terms, light can be shed on the cost of these measures","PeriodicalId":88226,"journal":{"name":"Chance (New York, N.Y.)","volume":"42 1","pages":"4 - 17"},"PeriodicalIF":0.0,"publicationDate":"2021-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73714755","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 : 2021-04-03DOI: 10.1080/09332480.2021.1915034
V. Nelson, Jason Crea
{"title":"The Data Science Instructional Escape Room-a Successful Experiment","authors":"V. Nelson, Jason Crea","doi":"10.1080/09332480.2021.1915034","DOIUrl":"https://doi.org/10.1080/09332480.2021.1915034","url":null,"abstract":"","PeriodicalId":88226,"journal":{"name":"Chance (New York, N.Y.)","volume":"36 1","pages":"53 - 58"},"PeriodicalIF":0.0,"publicationDate":"2021-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78897530","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 : 2021-04-03DOI: 10.1080/09332480.2021.1915033
Adam Palayew, S. Harper, J. Hanley
44 Large databases, such as the U.S. Fatality Analysis Reporting System (FARS), are often used to study factors that influence traffic crashes that result in injuries or deaths, and how they may be affected by human activities or environmental conditions. This genre of epidemiologic research presents considerable study design challenges, since crash rates vary by season, day of the week, and time of day. To minimize the effects of these extraneous factors, many studies use a double control matched design as laid out by Redelmeier and Tibshirani in the Journal of Clinical Epidemiology in 2017. This design identified the particular days (or portions of a day) in which the condition/factor of concern was present. The concern might relate to a national election or holiday, or Super Bowl Sunday, or the change to/from daylight savings time (DST). For each such day of concern, and for each year for which data are available, two comparison days—seven days before and seven days after—are identified. The notation for the counts of interest on these three days is shown in Figure 1. The statistical methods used to convert the summed counts (C, B, A) in Figure 1 into inferential Toward Reducing the Possibility of FalsePositive Results in Epidemiologic Studies of Traffic Crashes
{"title":"Toward Reducing the Possibility of False-Positive Results in Epidemiologic Studies of Traffic Crashes","authors":"Adam Palayew, S. Harper, J. Hanley","doi":"10.1080/09332480.2021.1915033","DOIUrl":"https://doi.org/10.1080/09332480.2021.1915033","url":null,"abstract":"44 Large databases, such as the U.S. Fatality Analysis Reporting System (FARS), are often used to study factors that influence traffic crashes that result in injuries or deaths, and how they may be affected by human activities or environmental conditions. This genre of epidemiologic research presents considerable study design challenges, since crash rates vary by season, day of the week, and time of day. To minimize the effects of these extraneous factors, many studies use a double control matched design as laid out by Redelmeier and Tibshirani in the Journal of Clinical Epidemiology in 2017. This design identified the particular days (or portions of a day) in which the condition/factor of concern was present. The concern might relate to a national election or holiday, or Super Bowl Sunday, or the change to/from daylight savings time (DST). For each such day of concern, and for each year for which data are available, two comparison days—seven days before and seven days after—are identified. The notation for the counts of interest on these three days is shown in Figure 1. The statistical methods used to convert the summed counts (C, B, A) in Figure 1 into inferential Toward Reducing the Possibility of FalsePositive Results in Epidemiologic Studies of Traffic Crashes","PeriodicalId":88226,"journal":{"name":"Chance (New York, N.Y.)","volume":"6 1","pages":"44 - 52"},"PeriodicalIF":0.0,"publicationDate":"2021-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90804544","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 : 2021-04-03DOI: 10.1080/09332480.2021.1915036
N. Lazar, Hyunnam Ryu
An interesting feature of much modern Big Data is that the data we collect, or the data we want to analyze, are not necessarily in the traditional matrix or array form familiar from our textbooks. They may be coerced to such a format for relative ease of analysis, but this is not a strong justification. Past columns have explored new methods that exploit the natural structure of such data sets more directly. Topological data analysis (TDA) is one such method. Much daunting mathematics lies behind the methods of TDA, but it is possible to gain an idea and understanding of the approach and its potential usefulness even without a deep dive into the intricacies of topology, homology classes, and the like. In fact, the basic idea is quite simple: to study data through their low-dimension topological features, which translate into connected components (dimension 0), loops (dimension 1), and voids (dimension 2). Higher dimensions do exist, but often do not contain much useful information. For threedimensional data, up to the second dimension topological features can be considered at most. A good analogy to make the meaning of these features concrete is a piece of Swiss cheese. The piece of cheese itself is one connected component. The holes that are apparent on the The Shape of Things: Topological Data Analysis
{"title":"The Shape of Things: Topological Data Analysis","authors":"N. Lazar, Hyunnam Ryu","doi":"10.1080/09332480.2021.1915036","DOIUrl":"https://doi.org/10.1080/09332480.2021.1915036","url":null,"abstract":"An interesting feature of much modern Big Data is that the data we collect, or the data we want to analyze, are not necessarily in the traditional matrix or array form familiar from our textbooks. They may be coerced to such a format for relative ease of analysis, but this is not a strong justification. Past columns have explored new methods that exploit the natural structure of such data sets more directly. Topological data analysis (TDA) is one such method. Much daunting mathematics lies behind the methods of TDA, but it is possible to gain an idea and understanding of the approach and its potential usefulness even without a deep dive into the intricacies of topology, homology classes, and the like. In fact, the basic idea is quite simple: to study data through their low-dimension topological features, which translate into connected components (dimension 0), loops (dimension 1), and voids (dimension 2). Higher dimensions do exist, but often do not contain much useful information. For threedimensional data, up to the second dimension topological features can be considered at most. A good analogy to make the meaning of these features concrete is a piece of Swiss cheese. The piece of cheese itself is one connected component. The holes that are apparent on the The Shape of Things: Topological Data Analysis","PeriodicalId":88226,"journal":{"name":"Chance (New York, N.Y.)","volume":"3 1","pages":"59 - 64"},"PeriodicalIF":0.0,"publicationDate":"2021-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74956747","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 : 2021-04-03DOI: 10.1080/09332480.2021.1915029
Brenda McIntire
{"title":"The Secret Career of Solomon Kullback","authors":"Brenda McIntire","doi":"10.1080/09332480.2021.1915029","DOIUrl":"https://doi.org/10.1080/09332480.2021.1915029","url":null,"abstract":"","PeriodicalId":88226,"journal":{"name":"Chance (New York, N.Y.)","volume":"153 1","pages":"18 - 23"},"PeriodicalIF":0.0,"publicationDate":"2021-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75055419","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 : 2021-04-03DOI: 10.1080/09332480.2021.1915030
T. Allen
24 Police officers are the mostvisible representatives of the U.S. legal system. They are close to the community and mostresponsible for direct intervention in cases of criminal activity. An attack against a police officer is an attack against the legal system, but the very nature of their responsibilities puts them in danger of attack. In 2019, 48 police officers were feloniously killed (FBI 2019 Police Officers Killed in the Line of Duty: A Correspondence Analysis of Circumstances and Time of Day
{"title":"Police Officers Killed in the Line of Duty: A Correspondence Analysis of Circumstances and Time of Day","authors":"T. Allen","doi":"10.1080/09332480.2021.1915030","DOIUrl":"https://doi.org/10.1080/09332480.2021.1915030","url":null,"abstract":"24 Police officers are the mostvisible representatives of the U.S. legal system. They are close to the community and mostresponsible for direct intervention in cases of criminal activity. An attack against a police officer is an attack against the legal system, but the very nature of their responsibilities puts them in danger of attack. In 2019, 48 police officers were feloniously killed (FBI 2019 Police Officers Killed in the Line of Duty: A Correspondence Analysis of Circumstances and Time of Day","PeriodicalId":88226,"journal":{"name":"Chance (New York, N.Y.)","volume":"6 1","pages":"24 - 30"},"PeriodicalIF":0.0,"publicationDate":"2021-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76430434","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 : 2021-01-02DOI: 10.1080/09332480.2021.1885939
C. Robert
A new edition of Principles of Uncertainty, the first edition of which I reviewed in JASA (2012), has appeared. I was asked by CRC Press to review the new book; here are some (almost raw) extracts from my report, removing the parts that were addressed by the author before the book went to print. Overall, my enthusiasm for the book, its original (and of course, subjective) defense of the Bayesian approach, and its highly enjoyable style, remains intact, especially when backed by the proof-in-the pudding 2017 Pragmatics of Uncertainty, which I reviewed in a 2019 issue of CHANCE (32(1)). In Chapter 6, the proof of the Central Limit Theorem uses the “smudge” technique, which is to add an independent noise to both the sequence of rvs and its limit. This is most effective and reminds me of quite a similar proof Jacques Neveu used in probability notes at the École Polytechnique, which went under the more-formal denomination of convolution, with the same (commendable) purpose of avoiding Fourier transforms. If anything, I would have favored a slightly more-condensed presentation in fewer than eight pages. In Chapter 7, I found a nice mention of (Hermann) Rubin’s insistence on not separating probability and utility because only the product matters. And another fascinating quote from Keynes, not from his early statistician years, but in 1937 as an established economist:
《不确定性原理》(Principles of Uncertainty)的新版本已经出现，我在《JASA》(2012)中回顾了第一版。CRC出版社请我评论这本新书;以下是我报告中的一些(几乎是原始的)摘录，删除了作者在书付印前写过的部分。总的来说，我对这本书的热情，它对贝叶斯方法的原始(当然是主观的)辩护，以及它非常令人愉快的风格，都保持不变，特别是在我在2019年的《CHANCE》(32(1))上评论过的《2017年不确定性语用学》(proof-in- in-the pudding)的支持下。在第6章中，中心极限定理的证明使用了“涂抹”技术，即在rv序列及其极限中添加独立噪声。这是最有效的，让我想起了Jacques Neveu在École理工学院的概率笔记中使用的一个非常相似的证明，它以更正式的形式命名卷积，同样(值得称赞的)目的是避免傅里叶变换。如果有什么不同的话，我更喜欢在8页以内做一个稍微简洁一点的陈述。在第7章中，我很好地提到(赫尔曼)鲁宾坚持不把概率和效用分开，因为只有产品才重要。凯恩斯的另一句名言，不是他早年做统计学家的时候说的，而是在1937年成为知名经济学家时说的:
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