BIOSTATISTICS--MODELING AND INFERENCE

我们提出的研究都与极端 计算机密集型统计应用于癌症研究，其他 医学问题和生理学。 Olshen提出用树结构方法研究问题， 样品再利用技术。 特别是，他建议研究和 实现一种用于产生无偏估计的技术 在分类树的终端节点内，并研究 样品再利用方法的分层和重新设计。 树结构方法渐近结果的研究 这包括分裂规则的适应性和 并给出了算法的收敛性。 Fredkin和Rice建议开发方法， 恢复有噪声的单通道膜片钳数据。 各种 在这方面出现的问题：正确处理过滤通过 反卷积，基于数据的平滑参数估计， 算法和计算机代码的扩展以处理多个 电导水平通道，以及处理 忽明忽暗 将特别注意发展 高效的计算机代码 艾布拉姆森建议进一步研究递归技术， 在模型的条件分析中， 影响的数量（例如，基线测量） 不是主要的兴趣和少量的固定效应， 是. 他建议为通用开发抛光软件， 分布

The research we propose is all connected with extremely computer intensive statistics applied to cancer research, other medical problems and physiology. Olshen proposes studying problems in tree-structured methods and sample reuse techniques. In particular, he proposes study and implementation of a technique for producing unbiased estimates within the terminal nodes of a classification tree, and study of stratification and redesign of sample reuse methods. Investigations of asymptotic results on tree-structured methods that incorporate adaptiveness of splitting rules and rates of convergence for the algorithms are also proposed. Fredkin and Rice propose development of methodology for restoration of noisy single channel patch clamp data. A variety of issues arise in this regard: proper handling of filtering via deconvolution, data-based smoothing parameter estimates, extension of algorithms and computer code to handle multi conductance level channels, and special methods for dealing with flickering. Special attention will be given to the development of efficient computer code. Abramson proposes further study of a recursive technique for use in conditional analysis of models in which there are a large number of effects (for example, baseline measurements) that are not of primary interest and a small number of fixed effects that are. He proposes development of polished software for general distribution.