CORE--STATISTICAL ANALYSIS OF TOXICS MEASUREMENT

统计核心的主要作用是为研究项目和其他核心提供实验设计咨询和数据解释协助。我们还将为项目提供数据库支持，特别关注分析核心A和DNA微阵列核心c的支持。新的重点将放在生物信息学支持领域。我们将开发DNA微阵列数据的存储和解释方法，并协助模式识别系统与蛋白质组学和元组学方法的模式识别系统。此外，我们将为新兴的微阵列领域开发新的统计方法。我们将继续致力于开发免疫阵列和其他高密度信息系统的统计方法。技术，如贝叶斯估计，非线性优化设计，最大伪似然，和M估计将被使用。我们建议发展和改进分析危险化学品的统计方法和算法，并创造新的和改进的测量技术。这对于加速器质谱数据的分析尤其重要。我们将处理包括非线性校准、非恒定方差、可能的异常值、接近或低于检测限的值以及高维和大数据集在内的问题。这些目标将通过五个具体目标来实现，包括：在统计问题上与其他研究人员合作，为危险化学品研究中经常遇到的问题开发新的统计方法，在数值分析和数值优化中应用最先进的技术，开发高效、快速和可靠的计算机算法，以解决统计方法实施中的问题；开发软件，将上述统计方法学和数值方法的发展纳入实验室使用，协助处理结构复杂的数据文件引起的生物信息学问题，并开展外联活动，向环境和管理界转让新的统计技术。

The primary role of the statistics core will be to provide consultation on experimental design and assistance with data interpretation for the research projects and other cores. We also will provide database support to projects with particular attention to support of the Analytical Core A and the DNA microarray Core C. A new emphasis will be in the area of bioinformatic support. We will develop methods for the storage and interpretation of DNA microarray data and assist with pattern recognition systems with pattern recognition systems for proteomic and metanomic approaches. In addition we will develop new statistical methods for the emerging field of microarrays. We will continue our work on developing statistical methods for immunoarrays and other information systems with high density. Techniques such as Bayes estimation, non-linear optimal design, maximus pseudo likelihood, and M estimation will be employed. We propose the development and refinement of statistical methods and algorithms for the analysis of hazardous chemicals and for the creation of new and improved measurement techniques. This will be particularly important with the analysis of data from accelerator mass spectroscopy. We will deal with problems including non-linear calibration, non-constant variance, possible outliers, values near or below detection limits and high- dimensional and large data sets. These goals will be accomplished in five specific aims including: collaboration with other investigators with other investigators on statistical questions, develop new statistical methods for problems commonly encountered in hazardous chemical research, apply state-of-the-art techniques in numerical analysis and numerical optimization to develop efficient, fast, and reliable computer algorithms to solve problems in implementing statistical methods, develop software incorporating the above developments in statistical methodology and numerical methods for laboratory use, assist with bioinformatic problems induced by the complexly structured data files, and conduct outreach activities to transfer new statistical technology to the environmental and regulatory community.