Basic and Applied Statistical Inference

在过去的十年中，统计数据推断发挥了重要作用。 在确立用于公众的基本事实方面的重要作用 政策，例如估计艾滋病毒感染的流行率， 确定职业或环境因素导致癌症的风险 在法律的环境中，例如， 就业歧视。 该项目的目的是 研究这些背景下出现的统计问题， 为解决这些问题制定适当的统计方法。 罕见病患病率估计方法的研究 通过合并个体样本来保持匿名的疾病 在测试前进行批量生产是一个主要目标。 这些方法 应使卫生专家能够获得合作， 受感染者和未受感染者， 错误分类（假阳性）将不会被识别。 鲁棒 方法，导致统计上有效的推论时，几个 科学上合理的模型可能是数据的基础， 少数意见可能是错误的，将继续强调。 此外，统计方法继续发挥作用， 就业方面的薪酬、晋升和聘用数据分析 歧视案件。 特别是替代回归 将分析缔约方在《公约》中使用的各种方法， 沃森诉沃斯堡银行案。

Over the last decade statistical data inference have played an important role in establishing the basic facts used for public policy, e.g. estimating the prevalence of HIV infection, determining the risk of cancer from occupational or environmental exposures, and in the legal setting, e.g. cases concerning employment discrimination. The purpose of this project is to study statistical problems arising in these contexts and to develop appropriate statistical methodologies for their solution. Research on procedures for estimating the prevalence of rare diseases which preserve anonymity by combining individual samples into batches prior to testing is a major goal. These methods should enable health specialists to obtain the cooperation of the public as infected individuals and non-infected persons who are misclassified (false positive) will not be identified. Robust methods which led to statistically valid inferences when several scientifically plausible models may underlie the data or when a few observations may be erroneous will continue to emphasized. In addition, statistical methods continue to play a role in the analysis of pay, promotion and hiring data in employment discrimination cases. In particular, alternative regression approaches will be analyzed that were used by parties in the Watson v. Fort Worth Bank case.