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通过折中回归权重改进小域估计AI 解读

Shrinkage estimates of small domain parameters typically use a combination of a noisy “direct” estimate that only uses data from a specific small domain and a more stable regression estimate. When the regression model is misspecified, estimation performance for the noisier domains can suffer due to substantial shrinkage toward a poorly estimated regression surface. In this article, we introduce a new class of robust, empirically-driven regression weights that target…

因果模型中对百分位不变性随机偏离的处理AI 解读

Summary Consider a clinical trial in which participants are randomized to a single-dose treatment or a placebo control and assume that the adherence level is accurately recorded. If the treatment is effective, then good adherers in the treatment group should do better than poor adherers because they received more drug; the treatment group data follow a dose–response curve. But, good adherers to the placebo often do better than poor adherers, so the observed…

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两阶段层级模型中的三目标估计AI 解读

Summary The beauty of the Bayesian approach is its ability to structure complicated models, inferential goals and analyses. To take full advantage of it, methods should be linked to an inferential goal via a loss function. For example, in the two-stage, compound sampling model the posterior means are optimal under squared error loss. However, they can perform poorly in estimating the histogram of the parameters or in ranking them. ‘Triple-goal’ estimates are motivated by the…

二元数据一般随机效应模型的极大似然一致性AI 解读

We consider a general random effects model for repeated binary measures, assuming a latent linear model with any class of mixing distributions. The latent model is assumed to have the Laird-Ware structure, but the random effects may be from any specified class of multivariate distributions and the error vector may have any specified continuous distribution. Elementwise threshold crossing then gives the observed vector of binary outcomes. Special cases of this model include…

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尺度变换随机效应模型的生存分析AI 解读

Frailty models are effective in broadening the class of survival models and inducing dependence in multivariate survival distributions. In proportional hazards, the random effect multiplies the hazard function. The scale-change model incorporates unobserved heterogeneity through a random effect that enters the baseline hazard function to change the time scale. We interpret this random effect as frailty, or other unobserved risks that create heterogeneity in the population.…

基于时间尺度变换随机效应模型的生存分析AI 解读

Frailty models are effective in broadening the class of survival models and inducing dependence in multivariate survival distributions. In proportional hazards, the random effect multiplies the hazard function. The scale-change model incorporates unobserved heterogeneity through a random effect that enters the baseline hazard function to change the time scale. We interpret this random effect as frailty, or other unobserved risks that create heterogeneity in the population.…

广义最小二乘估计量的影响分析AI 解读

Influence analysis and leverage analysis are important and well-established adjuncts to ordinary least squares (OLS) regression, but analogous regression diagnostics are not generally available for multivariate regression problems. This article describes measures of influence and leverage for a generalized least squares (GLS) estimator of the regression coefficients in a class of multivariate linear models for repeated measurements. When the covariance matrix of the…

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利用贝叶斯与经验贝叶斯方法估计参数总体AI 解读

In standard Bayes and empirical Bayes component decision problems, estimating inidividual parameters is the primary goal. In multiple comparison problems and in comparisons of histograms of estimates, however, the primary goal is to produce parameter estimates that can be considered as an ensemble. For example, the histogram of estimates should be a good estimate of the histogram of parameters. Standard methods of estimating by the posterior expectation do minimize…

不完整数据问题中的近似后验分布AI 解读

Summary We consider the problem of developing a simple approximation to a posterior distribution arising from incomplete data sampling. We compare approximations based on the normal distribution and upon conjugate distributions, theoretically where feasible and in some numerical examples. Two general conclusions can be made on the basis of our numerical work. First, when there is missing data, approximations which fail to take loss of information into account give overly…

使用 EM 算法时的观测信息矩阵计算AI 解读

Summary A procedure is derived for extracting the observed information matrix when the EM algorithm is used to find maximum likelihood estimates in incomplete data problems. The technique requires computation of a complete-data gradient vector or second derivative matrix, but not those associated with the incomplete data likelihood. In addition, a method useful in speeding up the convergence of the EM algorithm is developed. Two examples are presented.

加速失效时间模型的非参数分析AI 解读

Survival distributions can be characterized by and compared through their hazard functions. Tests using a proportional hazards model have good power if the two hazards do not cross, but without time-dependant covariates can have low power if they do. The method contained herein is designed to provide a complement to the proportional hazards model. Differences in survival distributions are parameterized by a scale change and the log rank statistic is used to generate an…

加速失效时间模型的非参数分析AI 解读

Survival distributions can be characterized by and compared through their hazard functions. Tests using a proportional hazards model have good power if the two hazards do not cross, but without time-dependant covariates can have low power if they do. The method contained herein is designed to provide a complement to the proportional hazards model. Differences in survival distributions are parameterized by a scale change and the log rank statistic is used to generate an…