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方面的协助。我们还将为项目提供数据库支持，特别关注对分析核心A和DNA微阵列核心C的支持。一个新的重点将放在生物信息支持领域。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模式识别系统的蛋白质组学和元组学方法。此外，我们还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。将采用贝叶斯估计、非线性最优设计、最大伪似然和M估计等技术。我们建议开发和改进用于危险化学品分析的统计方法和算法，并创建新的和改进的测量技术。这将是特别重要的数据分析从加速器质谱。我们将处理的问题，包括非线性校准，非恒定的方差，可能的离群值，接近或低于检测限的值和高维和大型数据集。这些目标将通过五个具体目标实现，包括：与其他研究人员合作，与其他研究人员在统计问题上，开发新的统计方法，在危险化学品研究中遇到的问题，应用国家的最先进的技术，在数值分析和数值优化，以开发有效的，快速的，可靠的计算机算法，以解决在实施统计方法的问题，开发软件，将上述统计方法和数值方法的发展纳入实验室使用，协助解决结构复杂的数据文件引起的生物信息问题，并开展外联活动，向环境和监管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.