Longitudinal data analysis : autoregressive linear mixed effects models
著者
書誌事項
Longitudinal data analysis : autoregressive linear mixed effects models
（Springer Briefs in statistics, . JSS research series in statistics / editors-in-chief,
Springer, c2018
- : [pbk.]
大学図書館所蔵 件 / 全4件
-
該当する所蔵館はありません
- すべての絞り込み条件を解除する
注記
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
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.
「Nielsen BookData」 より