ABASH 发表于 2025-3-28 15:24:42

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不爱防注射 发表于 2025-3-28 19:07:04

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脱毛 发表于 2025-3-29 00:47:49

Exploring Serial Correlation,ess, especially in the presence of random effects. This is because the residual variability is in practice very often dominated by the random effects in the model. In this chapter, we will discuss two procedures for exploring the residual covariance, conditionally on a set of random effects already

不吉祥的女人 发表于 2025-3-29 03:23:42

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Hiatal-Hernia 发表于 2025-3-29 10:43:15

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Inelasticity 发表于 2025-3-29 15:06:21

Conditional Linear Mixed Models,udinal studies, when compared to cross-sectional studies, is that they can distinguish changes over time within individuals (longitudinal effects) from differences among people in their baseline values (cross-sectional effects).

同谋 发表于 2025-3-29 18:59:03

Exploring Incomplete Data,vel standpoint (Figures 4.1 and 4.5) as well as from the population-averaged or group-averaged perspective (Figures 4.2, 4.3, 4.4, and 10.3). These plots are designed to focus on various structural aspects, such as the mean structure, the variance function, and the association structure.

Aura231 发表于 2025-3-29 22:28:39

Joint Modeling of Measurements and Missingness,d measurements data. Missing data are indeed common in clinical trials (Piantadosi 1997, Green, Benedetti, and Crowley 1997, Friedman, Furberg, and DeMets 1998), in epidemiologic studies (Kahn and Sempos 1989, Clayton and Hills 1993, Lilienfeld and Stolley 1994, Selvin 1996), and, very prominently,

Ostrich 发表于 2025-3-30 03:56:39

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Overstate 发表于 2025-3-30 06:17:10

Selection Models,layed a central role. This combines a marginal Gaussian regression model for the response, as might be used in the absence of missing data, with a Gaussian-based threshold model for the probability of a value being missing. For simplicity, consider a single Gaussian-distributed response variable ..
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查看完整版本: Titlebook: Linear Mixed Models for Longitudinal Data; Geert Verbeke,Geert Molenberghs Book 2000 Springer Science+Business Media New York 2000 Fitting