Discussion of “Co-citation and Co-authorship Networks of Statisticians” by Pengsheng Ji, Jiashun Jin, Zheng Tracy Ke, and Wanshan Li

IF 2.9 2区 数学 Q1 ECONOMICS Journal of Business & Economic Statistics Pub Date : 2022-04-03 DOI:10.1080/07350015.2022.2041423
Peter Macdonald, E. Levina, Ji Zhu
{"title":"Discussion of “Co-citation and Co-authorship Networks of Statisticians” by Pengsheng Ji, Jiashun Jin, Zheng Tracy Ke, and Wanshan Li","authors":"Peter Macdonald, E. Levina, Ji Zhu","doi":"10.1080/07350015.2022.2041423","DOIUrl":null,"url":null,"abstract":"We congratulate the authors on an interesting paper and on making an important contribution to the network analysis community through compiling a large new dataset which will spur further work on multilayer, dynamic and other complex network settings. This discussion focuses on the paper’s particular methods and applications in dynamic network analysis. Complexity of dynamic network data leads to many necessary analyst choices in both data processing and network modeling. Where possible, we will compare the choices made in this paper with other possibilities from recent literature on dynamic network analysis. One of the important points of the paper is that much of our network data has always been dynamic. For instance, communication networks consisting of sent and received E-mails come with time stamps, whether we choose to incorporate them or not. Developing statistical methods that take advantage of this time varying structure will lead to greater efficiency, novel insights, and generally allow us to take full advantage of rich modern datasets like the one featured in this paper.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"40 1","pages":"492 - 493"},"PeriodicalIF":2.9000,"publicationDate":"2022-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Business & Economic Statistics","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1080/07350015.2022.2041423","RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0

Abstract

We congratulate the authors on an interesting paper and on making an important contribution to the network analysis community through compiling a large new dataset which will spur further work on multilayer, dynamic and other complex network settings. This discussion focuses on the paper’s particular methods and applications in dynamic network analysis. Complexity of dynamic network data leads to many necessary analyst choices in both data processing and network modeling. Where possible, we will compare the choices made in this paper with other possibilities from recent literature on dynamic network analysis. One of the important points of the paper is that much of our network data has always been dynamic. For instance, communication networks consisting of sent and received E-mails come with time stamps, whether we choose to incorporate them or not. Developing statistical methods that take advantage of this time varying structure will lead to greater efficiency, novel insights, and generally allow us to take full advantage of rich modern datasets like the one featured in this paper.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
纪鹏生、金家顺、柯郑翠、李万山对“统计学家共引合著网络”的探讨
我们祝贺作者发表了一篇有趣的论文,并对网络分析社区做出了重要贡献,他们编纂了一个大型的新数据集,这将促进对多层、动态和其他复杂网络设置的进一步研究。本文着重讨论了本文在动态网络分析中的具体方法和应用。动态网络数据的复杂性导致分析人员在数据处理和网络建模方面有许多必要的选择。在可能的情况下,我们将把本文所做的选择与近期动态网络分析文献中的其他可能性进行比较。本文的重点之一是我们的网络数据一直是动态的。例如,由发送和接收电子邮件组成的通信网络带有时间戳,无论我们是否选择合并它们。开发利用这种时变结构的统计方法将带来更高的效率,新的见解,并且通常允许我们充分利用丰富的现代数据集,如本文所述的数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Journal of Business & Economic Statistics
Journal of Business & Economic Statistics 数学-统计学与概率论
CiteScore
5.00
自引率
6.70%
发文量
98
审稿时长
>12 weeks
期刊介绍: The Journal of Business and Economic Statistics (JBES) publishes a range of articles, primarily applied statistical analyses of microeconomic, macroeconomic, forecasting, business, and finance related topics. More general papers in statistics, econometrics, computation, simulation, or graphics are also appropriate if they are immediately applicable to the journal''s general topics of interest. Articles published in JBES contain significant results, high-quality methodological content, excellent exposition, and usually include a substantive empirical application.
期刊最新文献
A Ridge-Regularized Jackknifed Anderson-Rubin Test. Efficient and Robust Estimation of the Generalized LATE Model Modeling and Forecasting Macroeconomic Downside Risk* Causal inference under outcome-based sampling with monotonicity assumptions Functional-Coefficient Quantile Regression for Panel Data with Latent Group Structure
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1