• Summary
  • Contents
  • Subject index

In this important new Handbook, the editors have gathered together a range of leading contributors to introduce the theory and practice of multilevel modeling.

The Handbook establishes the connections in multilevel modeling, bringing together leading experts from around the world to provide a roadmap for applied researchers linking theory and practice, as well as a unique arsenal of state-of-the-art tools. It forges vital connections that cross traditional disciplinary divides and introduces best practice in the field.

Part I establishes the framework for estimation and inference, including chapters dedicated to notation, model selection, fixed and random effects, and causal inference; Part II develops variations and extensions, such as nonlinear, semiparametric and latent class models; Part III includes discussion of missing data and robust methods, assessment of fit and ...

Multilevel Functional Data Analysis
Multilevel functional data analysis
CiprianM.CrainiceanuJohns Hopkins University, USA
BrianS.CaffoJohns Hopkins University, USA
JeffreyS.MorrisThe University of Texas MD Anderson Cancer Center, USA
13.1 Introduction

The information explosion and increased access to ever larger and more complex data sets is one of the most important developments of the 21st century. Statistics, the science inspired by and developed for data analysis, is uniquely positioned to take advantage of these unprecedented opportunities. Thus, it has become crucial to reassess the scalability of existing statistical methods and develop new methods to analyze these emerging types of data.

For example, with the advent of new technologies, many studies now collect large-dimensional functional or imaging data on hundreds of subjects at multiple visits. Relevant examples from brain imaging and proteomics are included in Section ...

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