TREE-STRUCTURED STATISTICAL METHODS

自《分类与回归》一书出版以来 Trees，程序购物车，它体现了开发的树结构方法 在书中，已经得到了广泛的应用并取得了成功。尤其是，在那里 已经在诊断和预后问题上得到了实质性的应用 在癌症和心脏病学方面。这本书中的思想已扩展到包括 经过审查的生存数据，并已被用于评估 弥漫性肿瘤的剂量强度与其他预后因素的关系 大细胞淋巴瘤。此外，像小推车一样修剪大树到小树 One已被合并到新的有损数据压缩算法中 在数字射线照相术中。 目前的提议是研究新的能力，包括 树结构方法的进一步应用。研究领域 拥有广泛的范围，从在树上实现超级计算机速度 通过在工作站网络上使用分布式计算来构建 将树结构方法从数据压缩扩展到增强 淋巴瘤性纵隔肿块的磁共振成像。另外，我们 将可以访问从12,000多名受试者收集的数据集 为了比较新技术和旧技术在问题中的作用 快速诊断心肌梗死。进入医院的病人 主诉急性胸痛的急诊室。 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， 多响应回归、列联表分析、局部风险 估计，S g的树木，更有效的线性组合分裂， 样条线分割，树的抛光，密度估计和聚类， 分布式计算和两步向前看。

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.