Mixed Effects Models for Complex Data
Published on 11. November 2009
431 pages
978-1-4200-7408-6 (ISBN)
Description
Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors,
More details
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Researchers and graduate students in statistics and biostatistics; quantitative researchers in medicine, epidemiology, pharmaceutical science, and social science.
Illustrations
22 s/w Abbildungen, 19 s/w Tabellen
22 b/w images and 19 tables
File size
3,88 MB
ISBN-13
978-1-4200-7408-6 (9781420074086)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Book
09/2019
1st Edition
Chapman & Hall/CRC
€97.10
Shipment within 15-20 days
Book
11/2009
1st Edition
Chapman & Hall/CRC
€207.80
Shipment within 15-20 days
Person
Lang Wu is an associate professor in the Department of Statistics at the University of British Columbia in Vancouver, Canada.
Content
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.
