Nonparametric Models for Longitudinal Data (eBook, PDF)

With Implementation in R

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Nonparametric Models for Longitudinal Data with Implementations in R presents a comprehensive summary of major advances in nonparametric models and smoothing methods with longitudinal data. It covers methods, theories, and applications that are particularly useful for biomedical studies in the era of big data and precision medicine.

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"This book will be a good reference book in the area of longitudinal data. It provides a self-contained treatment of structured nonparametric models for longitudinal data. Three commonly used method-kernel, polynomial splines, penalization/smoothing splines are all treated with enough depth. The book's coverage of time-varying coefficient models is comprehensive. Shared-parameter and mixed-effects models are very useful in longitudinal data analysis. Section V 'Nonparametric Models for Distribution' summarizes recent development on a new class of models. This section alone will make the book unique among all published books in longitudinal data analysis. Another unique feature of the book is the use of four actual longitudinal studies