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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与人类活动相联系的高维信息处理过程中的一些统计方法。特别是，在这个过程中，我们把重点放在了日期适应或信息环境适应推理上。此外，在统计数据分析的过程中，我们提供了一些在数据调查和数据分析过程中未被注意到的成果。主要成果涉及树形结构方法、统计变换、统计图、数据自适应分布、一些混合模型以及比例风险模型的变体等。在项目研究中，我们获得了以下主要结果和一些富有成效的发现。我们系统地提供了高维处理，特别是数据科学领域的日期自适应推理过程和综合方法。在统计图形学、理论、方法、方法和数据科学领域中，我们获得了以下主要结果和一些建设性的发现系统地提供了应用和评价，并通过案例研究和模拟，提出和评价了数据自适应图形；针对医学领域的最新发展，提供了不完全数据的分析方法；提出了生存车和纵向MARS(L-MARS)，并将其应用于医学和医药领域文献中引用的一些数据。