Mathematical Sciences: Sequential Inference, Admissibility, and Invariance

将在若干统计领域进行研究： (1)马尔登将研究秩统计的非空模型， 新的排名程序的发展;（2）马丁塞克将工作 序列模型，用于确定估计量以及 位置和回归模型中的样本大小; 自适应随机近似中的斜率估计;以及 多阶段估计的渐近最优方法;（3） 斯托特的研究将是概率建模， 对感兴趣的数据结构进行统计分析，包括 那些在多项目反应测试中发现的， 潜在变量;（4）Wijsman的研究将包括研究 的顺序置信集，并开发了一种方法，使用交叉 在模型中推导分布的章节， 不变性变换 在一般统计领域的这项研究旨在 掌握各种数学和方法论 目前在努力扩大 统计到更广泛的各种应用。 包括的是 主要推动力是开发用于分析数据的顺序方法， 具有节约科学资源的潜力， 实验

Research will be conducted in several areas of statistics: (1) Marden will work on nonnull models for rank statistics and the development of new rank procedures; (2) Martinsek will work on sequential models for determining the estimator as well as the sample size in location and regression models; comparison of slope estimates in adaptive stochastic approximation; and asymptotically optimal procedures in multistage estimation; (3) Stout's research will be on probabilistic modeling and statistical analysis for interesting data structures, including those found in multiple item response tests and where there are latent variables; (4) Wijsman's research will include the study of sequential confidence sets and develop a method of using cross sections to deriving distributions in models that admit invariance transformations. This research in the general area of statistics aims to come to grips with a broad variety of mathematical and methodological problems that have been encountered in current efforts to extend statistics to a broader variety of applications. Included are major thrusts to develop sequential methods for analyzing data, with the potential for saving resources in scientific experimentation.