Correlated Data Analysis: Modeling, Analytics, and Applications

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Aimed at graduate students and researchers, this book deals with recent developments in correlated data analysis. It uses the class of dispersion models as marginal components in the formulation of joint models for correlated data. This enables the book to cover a broader range of data types than the traditional generalized linear models, such as correlated directional data and correlated compositional data. The reader is provided with a systematic treatment for the topic of estimating functions, and both generalized estimating equations (GEE) and quadratic inference functions (QIF) are studied as special cases. In addition to the discussions on marginal models and mixed-effects models, this book covers new topics on joint regression analysis based on Gaussian copulas and state space models for longitudinal data from long time series. Various real-world data examples, numerical illustrations and software usage tips are included too. Applied statisticians and data analysts in many subject-matter fields will find this text essential.

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