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Nonparametric Models for Longitudinal Data: With Implementation in R

Colin O Wu

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Description: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 derivationsWe have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Nonparametric Models for Longitudinal Data: With Implementation in R. To get started finding Nonparametric Models for Longitudinal Data: With Implementation in R, you are right to find our website which has a comprehensive collection of manuals listed.
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Pages: 552

Format: PDF, EPUB & Kindle Edition

Publisher: CRC Press

Release: 2018

ISBN: 0429939078

Description: 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 derivationsWe have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Nonparametric Models for Longitudinal Data: With Implementation in R. To get started finding Nonparametric Models for Longitudinal Data: With Implementation in R, you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

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Nonparametric Models for Longitudinal Data: With Implementation in R

Nonparametric Models for Longitudinal Data: With Implementation in R

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