Longitudinal Data Analysis : Autoregressive Linear Mixed Effects Models. 1st ed. 2018

種類:

電子ブック

責任表示:

by Ikuko Funatogawa, Takashi Funatogawa

出版情報:

Singapore : Springer Nature Singapore : Imprint: Springer, 2018

著者名:

• Funatogawa, Ikuko.
• Funatogawa, Takashi.
• SpringerLink (Online service)

シリーズ名:

JSS Research Series in Statistics ;

ISBN:

9789811000775 [9811000778]    

注記:

Chapter 1. Linear mixed effects model -- Chapter 2. Autoregressive linear mixed effects model -- Chapter 3. Bivariate longitudinal data -- Chapter 4. State-space representation -- Chapter 5. Missing data, time dependent covariate -- Chapter 6. Pretest-Posttest data.
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 als

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