Publisher's Synopsis
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. It also provides flexible tools to describe the temporal trends, covariate effects and correlation structures of repeated measurements in longitudinal data.
This book is intended for graduate students in statistics, data scientists and statisticians in biomedical sciences and public health. As experts in this area, the authors present extensive materials that are balanced between theoretical and practical topics. The statistical applications in real-life examples lead into meaningful interpretations and inferences.
Features:
Provides an overview of parametric and semiparametric methods
Shows smoothing methods for unstructured nonparametric models
Covers structured nonparametric models with time-varying coefficients
Discusses nonparametric shared-parameter and mixed-effects models
Presents nonparametric models for conditional distributions and functionals
Illustrates implementations using R software packages
Includes datasets and code in the authors' website
Contains asymptotic results and theoretical derivations
