Lang Wu

Mixed Effects Models for Complex Data (eBook, ePUB)

• Format: ePub

Produktbeschreibung

Presenting effective approaches to address missing data, measurement errors, censoring, and outliers in longitudinal data, this book covers linear, nonlinear, generalized linear, nonparametric, and semiparametric mixed effects models. It links each mixed effects model with the corresponding class of regression model for cross-sectional data and discusses computational strategies for likelihood estimations of mixed effects models. The author briefly describes generalized estimating equations methods and Bayesian mixed effects models and explains how to implement standard models using R and S-Plus. The real-world data examples used throughout encompass studies on mental distress, AIDS, and more.

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Produktdetails

• Verlag: Taylor & Francis
• Erscheinungstermin: 11. November 2009
• Englisch
• ISBN-13: 9781040171042
• Artikelnr.: 72583636

Autorenporträt

Lang Wu is an associate professor in the Department of Statistics at the University of British Columbia in Vancouver, Canada.

Inhaltsangabe

Introduction. Mixed Effects Models. Missing Data, Measurement Errors, and
Outliers. Mixed Effects Models with Missing Data. Mixed Effects Models with
Covariate Measurement Errors. Mixed Effects Models with Censoring. Survival
Mixed Effects (Frailty) Models. Joint Modeling Longitudinal and Survival
Data. Robust Mixed Effects Models. Generalized Estimating Equations (GEEs).
Bayesian Mixed Effects Models. Appendix. References. Index. Abstract.