A conversation with Nancy Reid

IF 0.8 4区 数学 Q3 STATISTICS & PROBABILITY Canadian Journal of Statistics-Revue Canadienne De Statistique Pub Date : 2022-12-23 DOI:10.1002/cjs.11750
Radu V. Craiu, Grace Y. Yi
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引用次数: 0

Abstract

Nancy Reid was born in September 1952 in Niagara Falls, Canada. She graduated from the University of Waterloo with a Bachelor in Mathematics and a Major in Statistics in 1974. She studied statistics at the University of British Columbia (UBC) where she obtained a Master's in Applied Mathematics in 1976, and at Stanford University where she graduated with a PhD in Statistics in 1979. After spending one year at Imperial College London visiting Sir David Cox, she joined UBC as an Assistant Professor in the Department of Mathematics, and in 1986 she moved to the University of Toronto as a faculty member in the Department of Statistics (now Statistical Sciences) where she has been ever since including serving as Chair between 1997 and 2002. At the time of writing, Nancy has authored over 100 papers and 5 books, including seminal developments in conditional inference, higher-order asymptotics, composite likelihood, and Bayesian inference. Her outstanding contributions to statistics have been recognized nationally and internationally with many awards, including the President's Award of the Committee of Presidents of Statistical Societies (COPSS), the Gold Medal awarded by the Statistical Society of Canada (SSC), and being elected Foreign Associate of the National Academy of Sciences. In 2017, the International Statistical Review published Nancy's conversation with Ana Maria Staicu [Staicu, A. M. (2017). Interview with Nancy Reid. International Statistical Review, 85(3), 381-403.], which had a biographical emphasis. Since then, Nancy has continued to support the discipline of statistics in important ways, such as by serving as Director of the Canadian Statistical Sciences Institute (CANSSI) (2015–2019) and Co-chair of the Institute of Mathematical Statistics' Committee on Ethics (2018–2020). Her research activity continues to be celebrated with important awards such as Fellowship of the Royal Society of London (2018), the inaugural Hollander Distinguished Lectureship at Florida State University (2020), the Distinguished Achievement Award (and Lectureship) from COPSS (2022), and the Guy Medal in Gold from the Royal Statistical Society (2022). In May 2022, the Department of Statistical Sciences at the University of Toronto, in collaboration with CANSSI and the SSC, organized a one-day conference, “Statistics at Its Best”, in honour of Nancy's 70th birthday. This conversation took place in Toronto around the time of the event. Its focus is on Nancy's views on building a career in statistics, and the challenges and opportunities statisticians encounter within the rapidly evolving data science ecosystem.

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与Nancy Reid的对话
南希·里德1952年9月出生于加拿大尼亚加拉大瀑布。1974年,她毕业于滑铁卢大学,获得数学学士学位和统计学专业。她曾在不列颠哥伦比亚大学(UBC)学习统计学,1976年获得应用数学硕士学位,1979年毕业于斯坦福大学,获得统计学博士学位。在伦敦帝国理工学院(Imperial College London)访问了大卫·考克斯爵士(Sir David Cox。在撰写本文时,Nancy已经撰写了100多篇论文和5本书,包括条件推理、高阶渐近性、复合似然和贝叶斯推理的开创性发展。她在统计方面的杰出贡献得到了国家和国际的认可,获得了许多奖项,包括统计学会主席委员会主席奖、加拿大统计学会金奖,以及当选为美国国家科学院外籍院士。2017年,《国际统计评论》发表了Nancy与Ana Maria Staicu的对话[Staicu,A.M.(2017)。对Nancy Reid的采访。《国际统计综述》,85(3),381‐403.],重点是传记。自那以后,Nancy继续以重要方式支持统计学学科,例如担任加拿大统计科学研究所(CANSSI)所长(2015-2019)和数理统计研究所伦理委员会联合主席(2018-2020)。她的研究活动继续受到重要奖项的庆祝,如伦敦皇家学会奖学金(2018年)、佛罗里达州立大学首届霍兰德杰出讲师奖(2020年)、COPSS杰出成就奖(和讲师奖)(2022年)和英国皇家统计学会盖伊金质奖章(2022)。2022年5月,多伦多大学统计科学系与加拿大国家统计研究所和SSC合作,组织了一次为期一天的会议“最佳统计”,以纪念南希70岁生日。这段对话发生在活动前后的多伦多。它的重点是南希对建立统计职业生涯的看法,以及统计学家在快速发展的数据科学生态系统中遇到的挑战和机遇。
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来源期刊
CiteScore
1.40
自引率
0.00%
发文量
62
审稿时长
>12 weeks
期刊介绍: The Canadian Journal of Statistics is the official journal of the Statistical Society of Canada. It has a reputation internationally as an excellent journal. The editorial board is comprised of statistical scientists with applied, computational, methodological, theoretical and probabilistic interests. Their role is to ensure that the journal continues to provide an international forum for the discipline of Statistics. The journal seeks papers making broad points of interest to many readers, whereas papers making important points of more specific interest are better placed in more specialized journals. The levels of innovation and impact are key in the evaluation of submitted manuscripts.
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Issue Information Issue Information Issue Information Censored autoregressive regression models with Student-t innovations Acknowledgement of referees' services remerciements aux membres des jurys
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