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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 ...

Smoothing and Semiparametric Models
Smoothing and semiparametric models
Jin-TingZhangNational University of Singapore, Singapore1
17.1 Introduction

Longitudinal responses with a number of covariates are frequently observed in various fields. To explore the effects of the covariates on the longitudinal responses, various parametric models such as linear/nonlinear regression models and linear/nonlinear mixed-effects models have been proposed; see Chapters 5, 8, 9, 14, and 15 for interesting discussions and real data examples, and see Davidian and Giltinan (1995), Vonesh and Chinchilli (1997), Diggle, Heagerty, Liang, and Zeger (2002), Pinheiro and Bates (2009), and Verbeke and Molenberghs (2009) among others for more ideas and methodologies for longitudinal data analysis using parametric modeling. Parametric models are very useful for longitudinal data analysis since they provide a parsimonious description of the relationship between the ...

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