Handbook of Bayesian, Fiducial, and Frequentist Inference

Berger, James, Meng, Xiao-Li, Reid, Nancy

Handbook of Bayesian, Fiducial, and Frequentist Inference
  • 出版商: CRC
  • 出版日期: 2024-02-26
  • 售價: $6,700
  • 貴賓價: 9.5 折 $6,365
  • 語言: 英文
  • 頁數: 406
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 036732198X
  • ISBN-13: 9780367321987
  • 相關分類: 機率統計學 Probability-and-statistics

    • 海外代購書籍(需單獨結帳)

相關主題

商品描述

The emergence of data science, in recent decades, has magnified the need for efficient methodology for analyzing data and highlighted the importance of statistical inference. Despite the tremendous progress that has been made, statistical science is still a young discipline and continues to have several different and competing paths in its approaches and its foundations. While the emergence of competing approaches is a natural progression of any scientific discipline, differences in the foundations of statistical inference can sometimes lead to different interpretations and conclusions from the same dataset. The increased interest in the foundations of statistical inference has led to many publications, and recent vibrant research activities in statistics, applied mathematics, philosophy and other fields of science reflect the importance of this development. The BFF approaches not only bridge foundations and scientific learning, but also facilitate objective and replicable scientific research, and provide scalable computing methodologies for the analysis of big data. Most of the published work typically focusses on a single topic or theme, and the body of work is scattered in different journals. This handbook provides a comprehensive introduction and broad overview of the key developments in the BFF schools of inference. It is intended for researchers and students who wish for an overview of foundations of inference from the BFF perspective and provides a general reference for BFF inference.

Key Features:

  • Provides a comprehensive introduction to the key developments in the BFF schools of inference
  • Gives an overview of modern inferential methods, allowing scientists in other fields to expand their knowledge
  • Is accessible for readers with different perspectives and backgrounds

商品描述(中文翻譯)

數據科學的興起在近幾十年中放大了對於有效數據分析方法的需求，並突顯了統計推斷的重要性。儘管已經取得了巨大的進展，統計科學仍然是一個年輕的學科，並且在其方法和基礎上持續存在多種不同且相互競爭的路徑。雖然競爭性方法的出現是任何科學學科的自然進程，但統計推斷基礎的差異有時會導致對同一數據集的不同解釋和結論。對統計推斷基礎的興趣增加，促成了許多出版物的出現，最近在統計學、應用數學、哲學及其他科學領域的活躍研究活動反映了這一發展的重要性。BFF 方法不僅橋接了基礎與科學學習，還促進了客觀且可重複的科學研究，並提供了可擴展的計算方法來分析大數據。大多數已發表的工作通常集中於單一主題或主題，且這些工作散佈在不同的期刊中。本手冊提供了對 BFF 推斷學派關鍵發展的全面介紹和廣泛概述。它旨在為希望從 BFF 角度了解推斷基礎的研究人員和學生提供概覽，並作為 BFF 推斷的一般參考。

主要特點：
- 提供對 BFF 推斷學派關鍵發展的全面介紹
- 概述現代推斷方法，讓其他領域的科學家擴展他們的知識
- 使不同觀點和背景的讀者都能輕鬆理解

作者簡介

James Berger, PhD is the Arts and Sciences Distinguished Professor Emeritus of Statistics at Duke University. Dr. Berger received his PhD in mathematics from Cornell University in 1974. Among the awards and honors, Dr. Berger has received Guggenheim and Sloan Fellowships, the COPSS President's Award in 1985, the Sigma Xi Research Award at Purdue University for contribution of the year to science in 1993, the COPSS Fisher Lecturer in 2001, the Wald Lecturer of the IMS in 2007 and the Wilks Award from the ASA in 2015. He was elected as foreign member of the Spanish Real Academia de Ciencias in 2002, elected to the USA National Academy of Sciences in 2003, was awarded an honorary Doctor of Science degree from Purdue University in 2004, and became an Honorary Professor at East China Normal University in 2011.

Xiao-Li Meng, PhD is the Whipple V. N. Jones Professor of Statistics at Harvard University. Dr. Meng received his PhD in statistics from Harvard University. He is the Founding Editor-in-Chief of Harvard Data Science Review. In 2020 he was elected to the American Academy of Arts and Sciences. His interests range from the theoretical foundations of statistical inferences to statistical methods and computation.

Nancy Reid, PhD is a University Professor of Statistical Sciences at the University of Toronto. Dr. Reid received her PhD in statistics from Stanford University, and is a Fellow of the Royal Society, the Royal Society of Canada, the Royal Society of Edinburgh, and a Foreign Associate of the National Academy of Sciences. In 2015 she was appointed Officer of the Order of Canada. Her research interests include the foundations and theory of statistical inference.

Min-ge Xie, PhD is a Distinguished Professor at Rutgers, The State University of New Jersey. Dr. Xie received his PhD in Statistics from the University of Illinois at Urbana-Champaign (UIUC). He is the current Editor of The American Statistician and a co-founding Editor-in-Chief of The New England Journal of Statistics in Data Science. His research work on confidence distributions was described as a "grounding process with energy and insight." His research interests include statistical inference, foundations of data science, fusion learning, and interdisciplinary research.

作者簡介(中文翻譯)

詹姆斯·伯傑（James Berger），博士，是杜克大學統計學的藝術與科學榮譽教授。伯傑博士於1974年在康奈爾大學獲得數學博士學位。伯傑博士曾獲得多項獎項和榮譽，包括古根海姆獎學金和斯隆獎學金，1985年獲得COPSS總統獎，1993年獲得普渡大學的Sigma Xi研究獎，以表彰對科學的年度貢獻，2001年擔任COPSS費雪講者，2007年成為IMS的瓦爾德講者，2015年獲得ASA的威爾克斯獎。他於2002年當選西班牙皇家科學院外籍成員，2003年當選美國國家科學院院士，2004年獲得普渡大學榮譽科學博士學位，並於2011年成為華東師範大學的榮譽教授。

孟小利（Xiao-Li Meng），博士，是哈佛大學的威普爾·V·N·瓊斯統計學教授。孟博士在哈佛大學獲得統計學博士學位。他是《哈佛數據科學評論》的創始主編。2020年，他當選美國藝術與科學學院院士。他的研究興趣涵蓋統計推斷的理論基礎、統計方法及計算。

南希·瑞德（Nancy Reid），博士，是多倫多大學統計科學的大學教授。瑞德博士在史丹佛大學獲得統計學博士學位，並且是英國皇家學會、加拿大皇家學會、愛丁堡皇家學會的院士，以及美國國家科學院的外籍成員。2015年，她被任命為加拿大勳章的官員。她的研究興趣包括統計推斷的基礎和理論。

謝敏閣（Min-ge Xie），博士，是新澤西州羅格斯大學的傑出教授。謝博士在伊利諾伊大學香檳分校（UIUC）獲得統計學博士學位。他目前是《美國統計學家》的編輯，並且是《新英格蘭數據科學統計期刊》的共同創始主編。他在信心分佈方面的研究工作被形容為「具有能量和洞察力的基礎過程」。他的研究興趣包括統計推斷、數據科學的基礎、融合學習和跨學科研究。

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