The theory of growth data analysis and the development of its softwares

生长数据分析理论及其软件开发

基本信息

批准号：
10558036
负责人：
FUJIKOSHI Yasunori
金额：
$ 8.9万
依托单位：
Hiroshima University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B).
财政年份：
1998
资助国家：
日本
起止时间：
1998 至 2000
项目状态：
已结题

项目摘要

The purpose of this project is to development statistical methods and softwares for growth or repeated measures data. As one of the main results we introduced the growth curve model with explanatory variables which are hierarchically related, and gave some results on its statistical analysis. More precisely, we pointed an importantce of the model, and proposed estimation methods based on the maximum likelihood, tests for mean parameters, simultaneous confidence intervals for severale growth curves, tests for covarinace structures, model selection criteria, etc. (Ann.Inst.Statist.Math., Comm.Statist., Multivariate Anal.). For unblanced data, assuming random effect covariance structure, we derived an extention of the previous statistical inference procedures (submitted). Further, based on these results, we completed software 「"GROWT3" Ver 1.0 : Program correspond to Windows for statistical analysis on growth curve models with linear structures of parameters.」 (Megu Ohotaki, Akari Satoh, Kennichi Satoh and Yasunori Fujikoshi). Related to this software, we also completed software for estimating a mixture distribution.In this project we dealt with traking analysis of height and weight of human. We proposed growth curve fitting based on a non-linear model and obtained some new findings for relationships among biological parameters (Acta Med.Auxol., Environmentrics).As basic works related to this project, we examined modifications of model selection criteria and their basic properties. Especially, we derived a modified AIC criterion in multivariate linear model (Hiroshima Math.J.), and obtained some basic properties on cross- validation criterion in growth curve model. As another result, we proposed an estimation method for misclassification errors based on binary repeated measures (Biometrics).

该项目的目的是开发用于增长或重复测量数据的统计方法和软件。作为主要结果之一，我们引入了具有分层相关的爆炸性变量的增长曲线模型，并在其统计分析上给出了一些结果。更准确地说，我们指出了模型的重要性，并根据最大似然，平均参数的测试，几个生长曲线的简单置信区间，协变量结构的测试，模型选择标准等进行了提出的估计方法（Ann.In.Instatist.Statist.Math。，Comm.Statist。对于未造成的数据，假设随机效应协方差结构，我们得出了先前的统计推理程序（已提交）的程度。此外，基于这些结果，我们完成了软件“ growt3” ver 1.0：程序对应于具有参数线性结构的生长曲线模型的统计分析的窗口。基于非线性模型的曲线拟合，并获得了一些针对生物学参数之间关系的新发现（Acta Med.auxol。，环境技术）。作为与该项目相关的基本作品，我们检查了模型选择标准及其基本属性的修改。尤其是我们得出了一种修改的AIC INIC AIC Intial Inter Inter Intropiation Intropiriation On-blimoshiation intriation introfiriation introfiriate introfiriate introfiriate intriide intriid inid inid inid intiid and.j.j.j.j），J），J），J）生长曲线模型的标准。作为另一个结果，我们提出了一种基于二进制重复测量（生物识别）的错误分类错误的估计方法。