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Nonlinear Models For Repeated Measurement Data: The Ultimate Guide to Analyzing Complex Data Structures

Jorge Amado

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Published in Nonlinear Models For Repeated Measurement Data (Chapman Hall/CRC Monographs On Statistics And Applied Probability 62)

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Nonlinear Models for Repeated Measurement Data (Chapman Hall/CRC Monographs on Statistics and Applied Probability 62)

Nonlinear Models for Repeated Measurement Data (Chapman & Hall/CRC Monographs on Statistics and Applied Probability Book 62)

by Marie Davidian

4.7 out of 5

Language	:	English
File size	:	7737 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	370 pages

In the realm of data analysis, the examination of data collected over time, known as longitudinal data, presents unique challenges. Traditional statistical methods often fall short in capturing the intricate patterns and nonlinear relationships inherent in such data. To address this need, Nonlinear Models for Repeated Measurement Data emerges as an indispensable resource for researchers seeking to delve into the complexities of longitudinal data analysis.

This comprehensive guide provides a thorough exploration of nonlinear models, a powerful class of statistical techniques designed to model complex data structures. Written by renowned experts in the field, the book offers a lucid exposition of the theoretical foundations, practical applications, and cutting-edge developments in nonlinear modeling.

Key Features

In-depth coverage: The book delves into a wide range of nonlinear models, including mixed effects models, multilevel models, hierarchical linear models, generalized estimating equations, generalized linear mixed models, nonlinear growth models, nonlinear change models, and nonlinear transition models.
Real-world applications: Numerous real-world examples illustrate the practical utility of nonlinear models in various research domains, such as psychology, education, medicine, and social sciences.
Methodological rigor: The authors provide a rigorous treatment of the statistical underpinnings of nonlinear models, ensuring a deep understanding of the underlying principles.
Advanced techniques: The book introduces advanced topics such as longitudinal mediation, longitudinal moderation, and nonlinear structural equation modeling, pushing the boundaries of longitudinal data analysis.
User-friendly examples: Step-by-step examples using statistical software packages, such as R, SAS, and SPSS, facilitate the application of nonlinear models in practice.

Target Audience

Nonlinear Models for Repeated Measurement Data is an invaluable resource for:

Researchers in psychology, education, medicine, and social sciences who seek to analyze complex longitudinal data.
Statisticians and data analysts looking to expand their knowledge of nonlinear modeling techniques.
Graduate students embarking on research projects involving longitudinal data analysis.

Nonlinear Models for Repeated Measurement Data is a tour de force in the field of longitudinal data analysis. It empowers researchers with the knowledge and tools to unlock the secrets of complex data structures, enabling them to uncover hidden patterns, test intricate hypotheses, and gain deeper insights into the phenomena under investigation.

Nonlinear Models for Repeated Measurement Data (Chapman & Hall/CRC Monographs on Statistics and Applied Probability Book 62)

by Marie Davidian

4.7 out of 5