商品描述
Development in methodology on longitudinal data is fast. Currently, there are a lack of intermediate /advanced level textbooks which introduce students and practicing statisticians to the updated methods on correlated data inference. This book will present a discussion of the modern approaches to inference, including the links between the theories of estimators and various types of efficient statistical models including likelihood-based approaches. The theory will be supported with practical examples of R-codes and R-packages applied to interesting case-studies from a number of different areas.
Key Features:
-Includes the most up-to-date methods
-Use simple examples to demonstrate complex methods
-Uses real data from a number of areas
-Examples utilize R code
商品描述(中文翻譯)
發展於縱向數據的方法論進展迅速。目前，缺乏中級/高級的教科書來介紹學生和實務統計學家更新的相關數據推斷方法。本書將討論現代推斷方法，包括估計量理論與各種高效統計模型（包括基於似然的方法）之間的聯繫。理論將通過實際的 R 代碼和 R 套件的範例來支持，這些範例來自於多個不同領域的有趣案例研究。
主要特點：
- 包含最新的方法
- 使用簡單的範例來演示複雜的方法
- 使用來自多個領域的真實數據
- 範例利用 R 代碼
作者簡介
Professor Wang obtained his Ph.D. on dynamic optimization in 1991 (University of Oxford) and worked for CSIRO (2005-2010). Before returning to Australia, Professor Wang worked for the National University of Singapore (2001-2005) and Harvard University as Assistant Professor and Associate Professor (1998-2000) in biostatistics. He joined the University of Queensland in April 2010 as Chair Professor of Applied Statistics to lead the Centre for Applications in Natural Resource Mathematics and to promote applied statistics and mathematics. Currently, he is Capacity Building Professor in Data Science at Queensland University of Technology, Australia. Professor Wang has developed a number of novel statistical methodologies in longitudinal data analysis published by top statistical journals (Biometrika, Biometrics, Statistics in Medicine, Journal of the American Statistician Association, Annals of Statistics). His recent interests and successes include (1) 'working' likelihood approach for hyperparameter estimation and model selection, (2) integrating statistical learning and machine learning for dependent data analysis and (3) data-driven approach for robust estimation. More recently, he advocates 'working' likelihood approaches to parameter estimation but recognizing possibly a different likelihood that generating the observed data in inferencing. This has been found very useful in finding datadependent tuning parameters in robust estimation and hyper-parameters in machine learning algorithms.
Liya Fu obtained her Ph.D. in 2010 from Northeast Normal University. Currently she is Associate Professor of Statistics at Xi'an Jiaotong University. She worked briefly as a Postdoctoral Fellow at the University of Queensland after after two-years visiting student at CSIRO (2008-010), Australia. Dr. Fu mainly focuses on the methodologies for the analysis of longitudinal data and has published about 20 papers in international journals, including Biometrics, Statistics in Medicine, Journal of Multivariate Analysis. Professor Sudhir Paul obtained his PhD in 1976 (University of Wales). He worked as a Postdoctoral Fellow (University of Newcastle Upon Tyne, 1976- 1978) and a Lecturer (University of Kent at Canterbury, 1978-1982) before moving to Canada in 1982. He started as Assistant Professor at the University of Windsor and moved through all professorial ranks and finally in 2005 became distinguished University Professor. He became Fellow of the Royal Statistical Society in 1982 and Fellow of the American Statistical Association in 1986.
Professor Paul has developed many methodologies for the analyses of overdispersed and zero-inflated count data, longitudinal data, and familial data and published in most of the top-tier journals in statistics (Journal of the Royal Statistical Society, Biometrika, Biometrics, Journal of the American Statistician Association, Technometrics). Professor Paul supervised over 50 graduate students including 16 PhD students and has published over 100 papers.
作者簡介(中文翻譯)
王教授於1991年在牛津大學獲得動態優化的博士學位，並於2005年至2010年間在CSIRO工作。在回到澳洲之前，王教授曾於2001年至2005年間在新加坡國立大學工作，並於1998年至2000年間在哈佛大學擔任生物統計學的助理教授和副教授。他於2010年4月加入昆士蘭大學，擔任應用統計學的講座教授，負責領導自然資源數學應用中心，並推廣應用統計學和數學。目前，他是澳洲昆士蘭科技大學的數據科學能力建設教授。王教授在長期數據分析方面開發了多種新穎的統計方法，並在頂級統計期刊上發表（如Biometrika、Biometrics、Statistics in Medicine、Journal of the American Statistician Association、Annals of Statistics）。他最近的興趣和成就包括（1）用於超參數估計和模型選擇的「工作」似然方法，（2）將統計學習和機器學習整合用於依賴數據分析，以及（3）用於穩健估計的數據驅動方法。最近，他提倡在參數估計中使用「工作」似然方法，但認識到在推斷中可能存在生成觀察數據的不同似然。這在尋找穩健估計中的數據依賴調整參數和機器學習算法中的超參數方面被發現非常有用。
傅莉雅於2010年在東北師範大學獲得博士學位。目前她是西安交通大學的統計學副教授。她在昆士蘭大學短暫擔任博士後研究員，之前曾於2008年至2010年間在CSIRO擔任訪問學生。傅博士主要專注於長期數據分析的方法論，並在國際期刊上發表了約20篇論文，包括Biometrics、Statistics in Medicine、Journal of Multivariate Analysis。蘇迪爾·保羅教授於1976年在威爾士大學獲得博士學位。他曾擔任博士後研究員（泰恩河畔紐卡斯爾大學，1976-1978）和講師（肯特大學，1978-1982），然後於1982年移居加拿大。他在溫莎大學擔任助理教授，並逐步晉升至所有教授職位，最終於2005年成為傑出大學教授。他於1982年成為英國皇家統計學會的會士，並於1986年成為美國統計學會的會士。
保羅教授開發了許多用於分析過度分散和零膨脹計數數據、長期數據和家族數據的方法論，並在大多數頂級統計期刊上發表（如Journal of the Royal Statistical Society、Biometrika、Biometrics、Journal of the American Statistician Association、Technometrics）。保羅教授指導了超過50名研究生，包括16名博士生，並發表了超過100篇論文。
