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Longitudinal Data Analysis: Autoregressive Linear Mixed Effects Models 2018 Edition
Contributor(s): Funatogawa, Ikuko (Author), Funatogawa, Takashi (Author)

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 ISBN: 981100076X     ISBN-13: 9789811000768
Publisher: Springer
OUR PRICE: $61.74  

Binding Type: Paperback
Published: February 2019
Qty:
Temporarily out of stock - Will ship within 2 to 5 weeks
Additional Information
BISAC Categories:
- Mathematics | Probability & Statistics - General
- Computers | Mathematical & Statistical Software
Dewey: 519.5
Physical Information: 0.33" H x 6.14" W x 9.21" L (0.49 lbs) 141 pages
 
Descriptions, Reviews, Etc.
Publisher Description:
This book provides a new analytical approach for dynamic data repeatedly measured from multiple subjects over time. Random effects account for differences across subjects. Auto-regression in response itself is often used in time series analysis. In longitudinal data analysis, a static mixed effects model is changed into a dynamic one by the introduction of the auto-regression term. Response levels in this model gradually move toward an asymptote or equilibrium which depends on covariates and random effects. The book provides relationships of the autoregressive linear mixed effects models with linear mixed effects models, marginal models, transition models, nonlinear mixed effects models, growth curves, differential equations, and state space representation. State space representation with a modified Kalman filter provides log likelihoods for maximum likelihood estimation, and this representation is suitable for unequally spaced longitudinal data. The extension to multivariate longitudinal data analysis is also provided. Topics in medical fields, such as response-dependent dose modifications, response-dependent dropouts, and randomized controlled trials are discussed. The text is written in plain terms understandable for researchers in other disciplines such as econometrics, sociology, and ecology for the progress of interdisciplinary research.
 
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