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Longitudinal Data Analysis

- Autoregressive Linear Mixed Effects Models
Af: Ikuko Funatogawa, Takashi Funatogawa Engelsk Paperback

Longitudinal Data Analysis

- Autoregressive Linear Mixed Effects Models
Af: Ikuko Funatogawa, Takashi Funatogawa Engelsk Paperback
Tjek vores konkurrenters priser
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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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.
Produktdetaljer
Sprog: Engelsk
Sider: 141
ISBN-13: 9789811000768
Indbinding: Paperback
Udgave:
ISBN-10: 981100076X
Udg. Dato: 22 feb 2019
Længde: 0mm
Bredde: 155mm
Højde: 235mm
Forlag: Springer Verlag, Singapore
Oplagsdato: 22 feb 2019
Forfatter(e) Ikuko Funatogawa, Takashi Funatogawa


Kategori Sandsynlighedsregning og statistik


ISBN-13 9789811000768


Sprog Engelsk


Indbinding Paperback


Sider 141


Udgave


Længde 0mm


Bredde 155mm


Højde 235mm


Udg. Dato 22 feb 2019


Oplagsdato 22 feb 2019


Forlag Springer Verlag, Singapore

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