TREE-STRUCTURED STATISTICAL METHODS

自《分类与回归》一书出版以来， 树，程序CART，它体现了树结构的方法开发 在书中，它被广泛使用并取得了成功。 特别是 在诊断和预后问题上有着重要的应用 在癌症和心脏病学。 书中的思想已经扩展到涵盖 删失生存数据，并已被应用于评估 剂量强度与其他预后因素的关系 大细胞淋巴瘤 此外，车一样修剪大树，以较小的 已被纳入有损数据压缩的新算法中 在数字放射摄影中。 目前的建议是研究新的能力， 树结构方法的进一步应用。 涉及的领域有调研 有一个广泛的范围，从实现超级计算机的速度在树 通过在工作站网络上使用分布式计算来构建， 从数据压缩扩展树结构方法，以增强 纵隔淋巴瘤的磁共振图像。 另外我们 将可以访问从12,000多名受试者中收集的数据集 为了比较新技术与旧技术的问题， 快速诊断心肌梗塞。患者进入 急诊室，主诉急性胸痛。 对现有算法的特殊改进和新功能 包括但不限于时间序列和曲线识别， 多元响应回归，列联表分析，局部风险 估计，树的s g，更有效的线性组合分裂， 样条分裂，抛光树，密度估计和聚类， 分布式计算和两步先行。

Since the publication of the book Classification and Regression Trees, the program CART, which embodies tree-structured methods developed in the book, has been used widely and with success. In particular, there have been substantial applications to problems of diagnosis and prognosis in cancer and cardiology. Ideas in the book have been extended to cover censored survival data and have been applied to assessing the relationship of dose intensity and other prognostic factors in diffuse large cell lymphoma. Also, CART-like pruning of large trees to smaller ones has been incorporated into new algorithms for lossy data compression in digital radiography. The present proposal is for research on new capabilities for and further applications of tree-structured methods. The areas of research have a wide range, from achieving supercomputer speed in tree construction by using distributed computing on a workstation network to expanding tree-structured methods from data compression to enhance magnetic resonance images of lymphomatous mediastinal masses. Also, we will have access to a data set gathered from more than 12,000 subjects for purposes of comparing new techniques with old ones in the problem of quickly diagnosing myocardial infarction for. patients who enter the emergency room with chief complaint of acute chest pain. Particular improvements and new capabilities to existing algorithms include but are not limited to time series and curve recognition, multiple response regression, analysis of contingency tables, local risk estimation, s g of trees, more effective linear combination splits, spline splits, polishing trees, density estimation and clustering, distributed computing, and two-step lookahead.