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用于多维政治定位的 ℓ1 贝叶斯理想点模型AI 解读

Ideal point estimation methods in the social sciences lack a principled approach for identifying multidimensional ideal points. We present a novel method for estimating multidimensional ideal points based on ℓ1 distance. In the Bayesian framework, the use of ℓ1 distance transforms the invariance problem of infinite rotational turns into the signed perpendicular problem, yielding posterior estimates that contract around a small area. Our simulation shows that the proposed…

在整群随机设计中使用排序集抽样改进处理效应推断AI 解读

Xinlei Wanga, Johan Limb & Lynne Stokesa*a Department of Statistical Science, Southern Methodist University, Dallas, TX, USAb Seoul National University, Seoul, KoreaCONTACT Lynne Stokes slstokes@smu.edu Department of Statistical Science, Southern Methodist University, Dallas, TX 75275-0332.Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/r/JASA.Supplementary materials for this article are available online. Please go to…

基于条件数正则化的协方差估计AI 解读

Summary Estimation of high dimensional covariance matrices is known to be a difficult problem, has many applications and is of current interest to the larger statistics community. In many applications including the so-called ‘large p, small n’ setting, the estimate of the covariance matrix is required to be not only invertible but also well conditioned. Although many regularization schemes attempt to do this, none of them address the ill conditioning problem directly. We…

多元次序统计量的伴随变量及其判断后分层应用AI 解读

We generalize the definition of a concomitant of an order statistic in the multivariate case, develop general expressions for its density, and establish related properties. We study the concomitant of a normal random vector in detail and discuss methods for calculating its moments. Furthermore, we apply the theory to develop new estimators of the mean from a judgment poststratified sample, where poststrata are formed by rank classes of auxiliary variables. Our estimators are…

利用贝叶斯树形捕获再捕获模型构造后分层AI 解读

For the problem of dual system estimation, we propose a Bayesian treed capture-recapture model to account for heterogeneity of capture probabilities where individual auxiliary information is available. The model uses a binary tree to partition the covariate space into ‘homogeneous’ regions, within each of which the capture response can be described adequately by a simple model that assumes equal catchability. The attractive features of the proposed model include reduction of…