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11#
發(fā)表于 2025-3-23 12:36:58 | 只看該作者
12#
發(fā)表于 2025-3-23 15:34:25 | 只看該作者
Bayesian Influence Assessment,or, the posterior distributions of parameters in growth curve models (GCMs) with Rao’s simple covariance structure SCS and unstructured covariance UC are obtained analytically, respectively. A Baysian entropy, namely, Kullback—Leibler divergence (KLD), as mentioned in Subsection 4.1.2 in Chapter 4,
13#
發(fā)表于 2025-3-23 18:27:18 | 只看該作者
14#
發(fā)表于 2025-3-23 23:00:03 | 只看該作者
15#
發(fā)表于 2025-3-24 05:18:31 | 只看該作者
16#
發(fā)表于 2025-3-24 07:00:33 | 只看該作者
Fragestellung und Untersuchungskonzept,atrices of the estimates are considered. In general, the MLE of the regression coefficient is different from the generalized least square estimate (GLSE) discussed in Chapter 2, because the former is a nonlinear function of the response variable while the latter is linear. There is indeed a special
17#
發(fā)表于 2025-3-24 13:49:53 | 只看該作者
Logic Circuit Design with DGMOS Devices,ce approach. Under Rao’s simple covariance structure (SCS) discussed in Section 3.2 of Chapter 3 and unstructured covariance (UC), two of the most commonly encountered covariance structures for growth analysis, the multiple individual deletion model (MIDM) and the mean shift regression model (MSRM)
18#
發(fā)表于 2025-3-24 15:54:40 | 只看該作者
19#
發(fā)表于 2025-3-24 21:55:23 | 只看該作者
https://doi.org/10.1007/978-3-031-08778-3or, the posterior distributions of parameters in growth curve models (GCMs) with Rao’s simple covariance structure SCS and unstructured covariance UC are obtained analytically, respectively. A Baysian entropy, namely, Kullback—Leibler divergence (KLD), as mentioned in Subsection 4.1.2 in Chapter 4,
20#
發(fā)表于 2025-3-25 01:20:12 | 只看該作者
John A. Fuerst,Evgeny Sagulenko unstructured covariance (UC), from the Bayesian point of view. The fundamental idea behind this procedure is to replace likelihood displacement in likelihood-based local influence method (see Subsection 5.1.1 in Chapter 5) with a Bayesian entropy, for example, the Kullback—Leibler divergence (KLD)
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