RESEARCH OF STATISTICAL INFERENCE PROCESS IN THE HIGH-DIMENSIONAL INFORMATION PROCESSING

高维信息处理中的统计推理过程研究

基本信息

批准号：
14208024
负责人：
GOTO Masashi
金额：
$ 14.56万
依托单位：
OSAKA UNIVERSITY
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (A)
财政年份：
2002
资助国家：
日本
起止时间：
2002 至 2003
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-14208024/
关键词：
splitting pooling reduction reconstruction transformation date investigations date-adaptive methods statistical graphics

项目摘要

The purpose of the project is to research and develop some statistical methodologies in process of the high-dimensional information processing which connects with human activities. Particularly, in the process we have focused on date-adaptive or information environmental-adaptive inference. Further, in the process of statistical date analysis, we provided some productive findings that were unsighted in date investigations and reifications.Main productive results involved tree structured approaches, statistical transformations, statistical graphics, date adaptive distributions, some mixed models, and variants of the proportional hazard model etc. In the project research, we could obtain the following main results and some productive findings.We have provided the process of date-adaptive inference and the comprehensive approaches systematically in the high-dimensional processing, particularly, fields of the date science.In the statistical graphics, theories, methods, applications and evaluations were systematically provided and the data-adaptive graphics were proposed and evaluated though case-studies and simulations.Analytical methods of incomplete data were provided with respect to recent developments and widely applied in medical fields.Survival CART and longitudinal MARS (L-MARS) were proposed, developed, and applied to some data cited in literatures of medical and pharmaceutical fields.

该项目的目的是研究和开发一些与人类活动相关的高维信息处理过程中的统计方法。特别是，在此过程中，我们专注于日期自适应或信息环境自适应推断。此外，在统计日期分析的过程中，我们提供了一些富有成效的发现，这些发现在日期调查和重新配置中是看不见的。生产效果涉及树结构化方法，统计转换，统计图形，日期自适应分布，一些混合模型，混合模型以及比例危险模型的变体。系统地提供了系统地提供的统计图形，理论，方法，应用和评估的统计图形，理论，方法，应用和评估，并在系统上提供了系统地提供和评估数据适应性图形，但在统计图形，理论，方法，应用和评估中进行了系统的系统，提出和评估了数据适应性图形，并评估了对最新的发展和广泛应用于医疗领域的不完整数据的方法，并在医疗方面进行了广泛应用。开发并应用于医学和药物领域文献中引用的一些数据。