| 书目名称 | Longitudinal Data Analysis | | 副标题 | Autoregressive Linea | | 编辑 | Ikuko Funatogawa,Takashi Funatogawa | | 视频video | | | 概述 | Describes a new analytical approach for longitudinal data, autoregressive linear mixed effects models, in which dynamic models are induced by the auto-regression term.Provides state space representati | | 丛书名称 | SpringerBriefs in Statistics | | 图书封面 |  | | 描述 | 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 wr | | 出版日期 | Book 2018 | | 关键词 | Longitudinal; Mixed Effects; Autoregressive; Dynamic; State Space | | 版次 | 1 | | doi | https://doi.org/10.1007/978-981-10-0077-5 | | isbn_softcover | 978-981-10-0076-8 | | isbn_ebook | 978-981-10-0077-5Series ISSN 2191-544X Series E-ISSN 2191-5458 | | issn_series | 2191-544X | | copyright | The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2018 |
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发表于 2025-3-21 18:42:30
