Generalized Linear Mixed Models: Modern Concepts, Methods and Applications
Stroup, Walter W., Ptukhina, Marina, Garai, Julie
- 出版商: CRC
- 出版日期: 2024-05-21
- 售價: $3,510
- 貴賓價: 9.5 折 $3,335
- 語言: 英文
- 頁數: 648
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 1498755569
- ISBN-13: 9781498755566
海外代購書籍(需單獨結帳)
相關主題
商品描述
Generalized Linear Mixed Models: Modern Concepts, Methods, and Applications (2nd edition) presents an updated introduction to linear modeling using the generalized linear mixed model (GLMM) as the overarching conceptual framework. For students new to statistical modeling, this book helps them see the big picture - linear modeling as broadly understood and its intimate connection with statistical design and mathematical statistics. For readers experienced in statistical practice, but new to GLMMs, the book provides a comprehensive introduction to GLMM methodology and its underlying theory.
Unlike textbooks that focus on classical linear models or generalized linear models or mixed models, this book covers all of the above as members of a unified GLMM family of linear models. In addition to essential theory and methodology, this book features a rich collection of examples using SAS(R) software to illustrate GLMM practice. This second edition is updated to reflect lessons learned and experience gained regarding best practices and modeling choices faced by GLMM practitioners. New to this edition are two chapters focusing on Bayesian methods for GLMMs.
Key Features:
- Most statistical modeling books cover classical linear models or advanced generalized and mixed models; this book covers all members of the GLMM family - classical and advanced models.
- Incorporates lessons learned from experience and on-going research to provide up-to-date examples of best practices.
- Illustrates connections between statistical design and modeling: guidelines for translating study design into appropriate model and in-depth illustrations of how to implement these guidelines; use of GLMM methods to improve planning and design.
- Discusses the difference between marginal and conditional models, differences in the inference space they are intended to address and when each type of model is appropriate.
- In addition to likelihood-based frequentist estimation and inference, provides a brief introduction to Bayesian methods for GLMMs.
Walt Stroup is an Emeritus Professor of Statistics. He served on the University of Nebraska statistics faculty for over 40 years, specializing in statistical modeling and statistical design. He is a Fellow of the American Statistical Association, winner of the University of Nebraska Outstanding Teaching and Innovative Curriculum Award and author or co-author of three books on mixed models and their extensions.
Marina Ptukhina (Pa-too-he-nuh), PhD, is an Associate Professor of Statistics at Whitman College. She is interested in statistical modeling, design and analysis of research studies and their applications. Her research includes applications of statistics to economics, biostatistics and statistical education. Ptukhina earned a PhD in Statistics from the University of Nebraska-Lincoln, a Master of Science degree in Mathematics from Texas Tech University and a Specialist degree in Management from The National Technical University "Kharkiv Polytechnic Institute."
Julie Garai, PhD, is a Data Scientist at Loop. She earned her PhD in Statistics from the University of Nebraska-Lincoln and a bachelor's degree in Mathematics and Spanish from Doane College. Dr Garai actively collaborates with statisticians, psychologists, ecologists, forest scientists, software engineers, and business leaders in academia and industry. In her spare time, she enjoys leisurely walks with her dogs, dance parties with her children, and playing the trombone.
商品描述(中文翻譯)
《廣義線性混合模型:現代概念、方法和應用》(第二版)以廣義線性混合模型(GLMM)作為整體概念框架,提供了一個更新的線性建模介紹。對於新手統計建模的學生,本書幫助他們看到整體圖像-線性建模的廣泛理解以及與統計設計和數學統計的密切聯繫。對於有經驗的統計實踐讀者,但對GLMM新手,本書提供了GLMM方法論及其基礎理論的全面介紹。
與專注於傳統線性模型或廣義線性模型或混合模型的教科書不同,本書將所有這些作為統一的GLMM線性模型家族的成員進行了涵蓋。除了基本理論和方法論外,本書還提供了使用SAS(R)軟件的豐富實例來說明GLMM實踐。第二版更新以反映GLMM實踐者面臨的最佳實踐和建模選擇的經驗和教訓。本版新增了兩章關於GLMM的貝葉斯方法。
主要特點:
- 大多數統計建模書籍涵蓋傳統線性模型或高級廣義和混合模型;本書涵蓋GLMM家族的所有成員-傳統和高級模型。
- 結合經驗和持續研究的教訓,提供最新的最佳實踐實例。
- 說明統計設計和建模之間的聯繫:將研究設計轉化為適當模型的指南和深入示例,使用GLMM方法改進規劃和設計。
- 討論邊際模型和條件模型之間的差異,它們所要解決的推論空間的差異以及每種類型的模型適用的情況。
- 除了基於概然的頻率估計和推論外,還簡要介紹了GLMM的貝葉斯方法。
Walt Stroup是統計學的名譽教授。他在內布拉斯加大學統計學教職任職超過40年,專攻統計建模和統計設計。他是美國統計學會的會士,獲得內布拉斯加大學傑出教學和創新課程獎,並撰寫或合著了三本關於混合模型及其擴展的書籍。
Marina Ptukhina(Pa-too-he-nuh)博士是惠特曼學院統計學副教授。她對統計建模、研究研究的設計和分析以及其應用感興趣。她的研究包括統計學在經濟學、生物統計學和統計教育中的應用。Ptukhina在內布拉斯加大學林肯分校獲得統計學博士學位,德克薩斯科技大學獲得數學碩士學位,以及國家技術大學哈爾科夫理工學院的管理專業學位。
Julie Garai博士是Loop的數據科學家。她在內布拉斯加大學林肯分校獲得統計學博士學位,並在Doane學院獲得數學和西班牙語學士學位。Garai博士積極與學術界和工業界的統計學家、心理學家、生態學家、森林科學家、軟件工程師和商業領袖合作。在閒暇時間,她喜歡和狗一起悠閒散步,和孩子們舉辦舞會,並演奏長號。
作者簡介
Walt Stroup is an Emeritus Professor of Statistics. He served on the University of Nebraska statistics faculty for over 40 years, specializing in statistical modeling and statistical design. He is a Fellow of the American Statistical Association, winner of the University of Nebraska Outstanding Teaching and Innovative Curriculum Award and author or co-author of three books on mixed models and their extensions.
Marina Ptukhina (Pa-too-he-nuh), PhD, is an Associate Professor of Statistics at Whitman College. She is interested in statistical modeling, design and analysis of research studies and their applications. Her research includes applications of statistics to economics, biostatistics and statistical education. Ptukhina earned a PhD in Statistics from the University of Nebraska-Lincoln, a Master of Science degree in Mathematics from Texas Tech University and a Specialist degree in Management from The National Technical University "Kharkiv Polytechnic Institute."
Julie Garai, PhD, is a Data Scientist at Loop. She earned her PhD in Statistics from the University of Nebraska-Lincoln and a bachelor's degree in Mathematics and Spanish from Doane College. Dr Garai actively collaborates with statisticians, psychologists, ecologists, forest scientists, software engineers, and business leaders in academia and industry. In her spare time, she enjoys leisurely walks with her dogs, dance parties with her children, and playing the trombone.
作者簡介(中文翻譯)
Walt Stroup是統計學的名譽教授。他在內布拉斯加大學統計學系任教超過40年,專攻統計建模和統計設計。他是美國統計學會的會士,曾獲得內布拉斯加大學傑出教學和創新課程獎,並著有或合著三本關於混合模型及其擴展的書籍。
Marina Ptukhina(Pa-too-he-nuh)博士是惠特曼學院統計學的副教授。她對統計建模、研究研究的設計和分析以及其應用感興趣。她的研究包括統計學在經濟學、生物統計學和統計教育中的應用。Ptukhina在內布拉斯加大學林肯分校獲得統計學博士學位,並在德克薩斯科技大學獲得數學碩士學位,以及在國家技術大學哈爾科夫理工學院獲得管理專業學位。
Julie Garai博士是Loop的數據科學家。她在內布拉斯加大學林肯分校獲得統計學博士學位,並在Doane學院獲得數學和西班牙語學士學位。Garai博士積極與學術界和工業界的統計學家、心理學家、生態學家、森林科學家、軟體工程師和商業領袖合作。在閒暇時間,她喜歡和狗一起悠閒散步,和孩子們舉辦舞會,還喜歡吹奏長號。