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Linear and Generalized Linear Mixed Models and Their Applications: A Practical Guide for Data Scientists

Dylan Mitchell

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Published in Linear And Generalized Linear Mixed Models And Their Applications (Springer In Statistics)

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Linear and generalized linear mixed models (LMMs and GLMMs) are powerful statistical tools that can be used to analyze data with complex structures, such as repeated measures, clustered data, and longitudinal data. These models extend the traditional linear regression model by allowing for the inclusion of random effects, which can account for the correlation between observations within a group or cluster.

Linear and Generalized Linear Mixed Models and Their Applications (Springer in Statistics)

Linear and Generalized Linear Mixed Models and Their Applications (Springer Series in Statistics)

by Jiming Jiang

4.5 out of 5

Language	:	English
File size	:	4926 KB
Print length	:	271 pages

LMMs and GLMMs are widely used in a variety of fields, including biostatistics, epidemiology, social science, and economics. They are particularly useful for analyzing data that is collected over time or from multiple sources.

This book provides a comprehensive to LMMs and GLMMs. It covers a wide range of topics, including model formulation, estimation, and inference. It also provides numerous examples and case studies to illustrate the application of LMMs and GLMMs in real-world settings.

Audience

This book is intended for data scientists and other professionals who need to analyze data with complex structures. It is assumed that the reader has a basic understanding of statistics and linear regression.

Organization

The book is organized into four parts:

* Part I: to Linear and Generalized Linear Mixed Models * Part II: Model Formulation * Part III: Estimation and Inference * Part IV: Applications

Part I provides an overview of LMMs and GLMMs, and discusses their advantages and disadvantages. Part II covers the different types of LMMs and GLMMs, and how to choose the right model for a given dataset. Part III describes the different methods for estimating and testing LMMs and GLMMs. Part IV provides numerous examples and case studies to illustrate the application of LMMs and GLMMs in real-world settings.

Features

* Comprehensive coverage of LMMs and GLMMs * Numerous examples and case studies * Clear and concise explanations * Written by a team of experts in the field