Mathematical Sciences: Empirical Bayes Approaches to Multiparameter Estimation

这项研究将有助于方法， 构造经验贝叶斯置信区间和假设 测试，并将提供理论和/或模拟验证 这些统计数据。 调查人员建议扩大目前的 包括非高斯分布和向量技术 参数 许多经验性实验的一个共同特点是， 得出一系列相似参数的结论。 的 被称为经验贝叶斯统计方法已被证明是有用的 在这种情况下。 建议将每个项目视为 作为一个整体的一部分，从而实现相当大的 为集的收益。 目前的方法适用于非常 特殊类型的数据，即分布在形状中的数据 钟（Gaussian） 开发经验贝叶斯方法 对于各种类型的数据将增加信息 科学家们可以从他们的实验中获得。

This research will contribute to methodology for constructing empirical Bayes confidence intervals and hypothesis tests, and will provide theory and/or simulation to validate these statistics. The investigators propose to expand current technology to include non-Gaussian distributions and vector parameters. A common feature of many empirical experiments is the desire to draw conclusions about a series of similar parameters. The statistical approach known as Empirical Bayes has proved useful in this setting. The proposal is to view each item to be estimated as part of an ensemble, thereby achieving considerable gains for the set. Current methods are applicable to very special types of data, i.e. data that is distributed in the shape of a bell (Gaussian). Developing the Empirical Bayes approach for a variety of types of data will increase the information scientists can obtain from their experiments.