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Full Description
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
Contents
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
