Objective Bayesian inference procedures are derived for the parameters of the multivariate random effects model generalized to elliptically contoured distributions. The posterior for the overall mean vector and the between-study covariance matrix is deduced by assigning two noninformative priors to the model parameter, namely the Berger and Bernardo reference prior and the Jeffreys prior, whose analytical expressions are obtained under weak distributional assumptions. It is shown that the only condition needed for the posterior to be proper is that the sample size is larger than the dimension of the data-generating model, independently of the class of elliptically contoured distributions used in the definition of the generalized multivariate random effects model. The theoretical findings of the paper are applied to real data consisting of ten studies about the effectiveness of hypertension treatment for reducing blood pressure where the treatment effects on both the systolic blood pressure and diastolic blood pressure are investigated.

翻译：用于计算整体平均矢量和研究间共变矩阵的后方值,通过给模型参数指定两个非信息前端,即Berger和Bernardo之前的参考,和Jeffrey之前的参考,前者的分析表达方式是在分布假设薄弱的情况下获得的,显示后方的正确唯一必要条件是,样品尺寸大于数据生成模型的尺寸,而不受通用多变随机效应模型定义中使用的螺旋型分布的分类,该文件的理论结论适用于实际数据,其中包括10项关于高血压治疗对减少血压的效果的研究,其中调查对静脉血压和异心血压的处理影响。