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受限参数空间下协方差矩阵的 REML 估计AI 解读

Restricted parameter spaces for covariance matrices, such as ∑ = σ2 I or ∑ = αI + βJ, are often used to simplify estimation. In addition, fixed upper and/or lower bounds may be needed to ensure that estimates satisfy a priori hypotheses. With multivariate variance components models, several covariance matrices need to be simultaneously estimated and, even with a reduced parameter space, estimation can be difficult. In earlier work we have discussed estimation for a…

Löwner 序约束下协方差矩阵的最大似然估计及其方差分量应用AI 解读

The problem of maximum likelihood estimation of Lowner ordered covariance matrices is considered. It is shown that a dual formulation of this problem is tractable and important in its own right. The interplay between the primal and dual problems suggests a general algorithm for computing the solutions to these problems. This algorithm has application to some estimation problems in balanced multivariate variance components models. The speed of convergence is also discussed…

平衡多变量方差分量模型中协方差矩阵的最小二乘估计AI 解读

The problem of estimating covariance matrices in balanced multivariate variance components models is discussed. As with univariate models, it is possible for the traditional estimators, based on differences of the mean square matrices, to produce estimates that are outside the parameter space. In fact, in many cases it is extremely likely that traditional estimates of the covariance matrices will not be nonnegative definite (nnd). In this article we develop an iterative…

平衡多元方差分量模型中协方差矩阵的最小二乘估计AI 解读

The problem of estimating covariance matrices in balanced multivariate variance components models is discussed. As with univariate models, it is possible for the traditional estimators, based on differences of the mean square matrices, to produce estimates that are outside the parameter space. In fact, in many cases it is extremely likely that traditional estimates of the covariance matrices will not be nonnegative definite (nnd). In this article we develop an iterative…

医疗照护模式研究中的贝叶斯与频率学派预测推断AI 解读

The Patterns of Care Studies were conducted to determine the quality of care received by cancer patients whose primary treatment modality is radiation therapy. In this article, we propose and evaluate models which, if acceptable, permit Bayesian and frequentist model-based predictive inference for the desired finite population parameters. Using both hierarchical Bayesian and frequentist mixed linear models, we describe methodology for making the desired inferences,…

医疗照护模式研究中的贝叶斯与频率学派预测推断AI 解读

The Patterns of Care Studies were conducted to determine the quality of care received by cancer patients whose primary treatment modality is radiation therapy. In this article, we propose and evaluate models which, if acceptable, permit Bayesian and frequentist model-based predictive inference for the desired finite population parameters. Using both hierarchical Bayesian and frequentist mixed linear models, we describe methodology for making the desired inferences,…