Professor Wang obtained his Ph.D. on dynamic optimization in 1991 (University of Oxford) and worked for CSIRO (20052010). Before returning to Australia, Professor Wang worked for the National University of Singapore (20012005) and Harvard University as Assistant Professor and Associate Professor (19982000) 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 (2008010), 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.

Texte du rabat

Currently, there are a lack of intermediate /advanced level textbooks which introduce students and practicing statisticians to the updated methods on correlated data inference. T

Résumé

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

Contenu

Chapter 1 Introduction Chapter 2 Examples and Organization of The Book Chapter 3 Model Framework and Its Components Chapter 4 Parameter Estimation Chapter 5 Model Selection Chapter 6 Robust Approaches Chapter 7 Clustered Data Analysis Chapter 8 Missing Data Analysis Chapter 9 Random Effects and Transitional Models Chapter 10 Handing High Dimensional Longitudinal Data