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Longitudinal Data Analysis : Autoregressive Linear Mixed Effects Models - (Paperback)

Longitudinal Data Analysis : Autoregressive Linear Mixed Effects Models -  (Paperback) - image 1 of 1

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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.
Genre: Mathematics, Computers + Internet
Series Title: Jss Research Series in Statistics
Format: Paperback
Publisher: Springer Nature
Author: Ikuko Funatogawa & Takashi Funatogawa
Language: English
Street Date: February 22, 2019
TCIN: 53069759
UPC: 9789811000768
Item Number (DPCI): 248-54-3080
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MSRPReg: $59.99 Save $3.00 (5% off)
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