Computer-intensive statistical methods in multivariate analysis

多变量分析中的计算机密集型统计方法

基本信息

项目摘要

The purpose of the research is to investigate the computer intensive statistical methods in multivariate analysis. The followings are the main results obtained through this research project extending over two years.1.The bootstrap methods have been extensively studied for the past fifteen years and become powerful tools for constructing inferential procedures in complex situations. In regard to computerintensive methods like the bootstrap the following problems have been studied ; (1)the methods for predictive error estimation in discriminant analysis and pattern recognition, (2)the use of the bootstrap in the estimation of the biases and variances of maximum likelihood estimates in factor analysis, (3)the theoretical and practical aspects of the use of the bootstrap in model evaluation problems. 2.A consecutive-k-out-of-n : F system is an ordered sequence of n components. The results obtained through the research project are the followings ; (1)the relationship between the reliability … More of the systems and discrete distributions of order k, (2)number of occurrences of success runs of specified length in a two-state Markov chain. 3.Properties of limiting distributions which satisfy certain functional equations were investigated.4.Mantel-Haenszel type test statistics were proposed for testing whether a new treatment is at least as effective as the standard treatment in comparative binomial trials. 5.The determination of the no-observed-adverse-effect level was studied when observed response are from normal distribution. Several tests which incorporate order restriction were examined and a new method based on Akaike's information criterion was proposed. 6.An approximation problem by polynomial spline functions with one free knot was investigated, and it was shown that every spline function satisfying Braess's alternation condition is nearly optimal. 7.New test procedures were proposed for nonparametric two-sample testing problems for location, scale and higher order quantities. Less
本研究的目的是探讨多元分析中的计算机密集型统计方法。本研究历时两年多,取得了以下主要成果:1.自举方法在过去的十五年里得到了广泛的研究,并成为构造复杂情况下推理过程的有力工具。关于像Bootstrap这样的计算机密集型方法,已经研究了以下问题:(1)判别分析和模式识别中的预测误差估计方法,(2)Bootstrap在因子分析中最大似然估计的偏差和方差估计中的使用,(3)Bootstrap在模型评估问题中使用的理论和实践方面。2. n中取k:F系统是n个分量的有序序列。通过本课题的研究,得到了以下几点结论:(1)可靠性与 ...更多信息 (2)两状态马尔可夫链中给定长度的成功运行的出现次数。3.研究了满足一定函数方程的极限分布的性质。4.提出了Mantel-Haenszel型检验统计量,用于检验比较二项试验中新处理是否至少与标准处理一样有效。5.研究了当观察到的反应来自正态分布时无明显不良作用水平的确定。研究了几种引入阶数限制的检验方法,提出了一种基于赤池信息量准则的检验方法。6.研究了带一个自由结点的多项式样条函数的逼近问题,证明了满足Braess交替条件的样条函数都是近最优的。7.针对位置、尺度和高阶量的非参数两样本检验问题,提出了新的检验方法。少

项目成果

期刊论文数量(88)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Konishi,S.: "Maximum likelihood estimation of an intraclass correlation in a bivariate normal distribution with missing observations." Communications in Statistics-Theory and Methods. 23. 1593-1604 (1994)
Konishi,S.:“在缺失观测值的二元正态分布中类内相关性的最大似然估计。”
  • DOI:
  • 发表时间:
  • 期刊:
  • 影响因子:
    0
  • 作者:
  • 通讯作者:
Aki,S.: "Discrete distributions related to succession events in a two-state Markov chain." Statistical Sciences and Date Analysis (Eds.K.Matsusita et al.) VSP. 467-474 (1993)
Aki,S.:“与二态马尔可夫链中的继承事件相关的离散分布。”
  • DOI:
  • 发表时间:
  • 期刊:
  • 影响因子:
    0
  • 作者:
  • 通讯作者:
Yanagawa,T.: "Generalized Mantel-Haenszel procedures for 2 × J tables." Environmental Health Perspectives,. 102. 57-60 (1994)
Yanakawa, T.:“2 × J 表的广义 Mantel-Haenszel 程序。” 102. 57-60 (1994)
  • DOI:
  • 发表时间:
  • 期刊:
  • 影响因子:
    0
  • 作者:
  • 通讯作者:
Nitta,H.: "A new approach based on a covariance structure model to source apportionment of indoor fine particles in Tokyo." Atmospheric Environment. 28. 631-636 (1994)
Nitta,H.:“一种基于协方差结构模型的新方法,用于东京室内细颗粒物的来源分配。”
  • DOI:
  • 发表时间:
  • 期刊:
  • 影响因子:
    0
  • 作者:
  • 通讯作者:
Yanagawa,T.: "Statistical issues on the no-observed-adverse effect level in categorical response." Environmental Health Perspectives Supplements,. 102. 95-101 (1994)
Yanakawa,T.:“分类反应中未观察到不利影响水平的统计问题。”
  • DOI:
  • 发表时间:
  • 期刊:
  • 影响因子:
    0
  • 作者:
  • 通讯作者:
{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

KONISHI Sadanori其他文献

KONISHI Sadanori的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('KONISHI Sadanori', 18)}}的其他基金

Theoretical developments of sparse modeling and multivariate analysis techniques
稀疏建模和多元分析技术的理论发展
  • 批准号:
    16K00057
  • 财政年份:
    2016
  • 资助金额:
    $ 0.96万
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
Nonlinear modeling based on high-dimensional data
基于高维数据的非线性建模
  • 批准号:
    21300106
  • 财政年份:
    2009
  • 资助金额:
    $ 0.96万
  • 项目类别:
    Grant-in-Aid for Scientific Research (B)
Nonlinear multivariate analysis based on high-dimensional data and its application
基于高维数据的非线性多元分析及其应用
  • 批准号:
    17300089
  • 财政年份:
    2005
  • 资助金额:
    $ 0.96万
  • 项目类别:
    Grant-in-Aid for Scientific Research (B)
Mathematical modeling for high-dimensional nonlinear data and its application to the analysis of complex phenomena
高维非线性数据的数学建模及其在复杂现象分析中的应用
  • 批准号:
    13440034
  • 财政年份:
    2001
  • 资助金额:
    $ 0.96万
  • 项目类别:
    Grant-in-Aid for Scientific Research (B)
NONLINEAR STATISTICAL MODELING AND MODEL EVALUATION
非线性统计建模和模型评估
  • 批准号:
    09440082
  • 财政年份:
    1997
  • 资助金额:
    $ 0.96万
  • 项目类别:
    Grant-in-Aid for Scientific Research (B).
Evaluation of Predictive Distributions based on Information and Entropy
基于信息和熵的预测分布评估
  • 批准号:
    08454043
  • 财政年份:
    1996
  • 资助金额:
    $ 0.96万
  • 项目类别:
    Grant-in-Aid for Scientific Research (B)
Multivariate Statistical Methods for Nonnormal Populations
非正态总体的多元统计方法
  • 批准号:
    61530018
  • 财政年份:
    1986
  • 资助金额:
    $ 0.96万
  • 项目类别:
    Grant-in-Aid for General Scientific Research (C)

相似海外基金

Developing Information Criteria for Modern Statistics
制定现代统计信息标准
  • 批准号:
    23H00809
  • 财政年份:
    2023
  • 资助金额:
    $ 0.96万
  • 项目类别:
    Grant-in-Aid for Scientific Research (B)
Development of cell lineage inference method from cell atlas based on information criteria
基于信息标准的细胞图谱细胞谱系推断方法的发展
  • 批准号:
    21K19827
  • 财政年份:
    2021
  • 资助金额:
    $ 0.96万
  • 项目类别:
    Grant-in-Aid for Challenging Research (Exploratory)
Development of multiple classification and comparison methods of cells based on information criteria and their applications to Cell Atlas
基于信息标准的细胞多种分类和比较方法的开发及其在Cell Atlas中的应用
  • 批准号:
    19K22894
  • 财政年份:
    2019
  • 资助金额:
    $ 0.96万
  • 项目类别:
    Grant-in-Aid for Challenging Research (Exploratory)
Developing New Algebraic Geometric Information Criteria for Monte Carlo Inference and Model Selection in Latent Variable and Missing Data Problems
为潜变量和缺失数据问题中的蒙特卡罗推理和模型选择开发新的代数几何信息准则
  • 批准号:
    261488-2012
  • 财政年份:
    2017
  • 资助金额:
    $ 0.96万
  • 项目类别:
    Discovery Grants Program - Individual
Asymptotic properties of some information criteria for model selectin
模型选择的一些信息准则的渐近性质
  • 批准号:
    17K00042
  • 财政年份:
    2017
  • 资助金额:
    $ 0.96万
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
Developing New Algebraic Geometric Information Criteria for Monte Carlo Inference and Model Selection in Latent Variable and Missing Data Problems
为潜变量和缺失数据问题中的蒙特卡罗推理和模型选择开发新的代数几何信息准则
  • 批准号:
    261488-2012
  • 财政年份:
    2015
  • 资助金额:
    $ 0.96万
  • 项目类别:
    Discovery Grants Program - Individual
Information-Criteria-Based Model Selection with Missing Data
缺失数据的基于信息标准的模型选择
  • 批准号:
    470865-2014
  • 财政年份:
    2014
  • 资助金额:
    $ 0.96万
  • 项目类别:
    University Undergraduate Student Research Awards
Developing New Algebraic Geometric Information Criteria for Monte Carlo Inference and Model Selection in Latent Variable and Missing Data Problems
为潜变量和缺失数据问题中的蒙特卡罗推理和模型选择开发新的代数几何信息准则
  • 批准号:
    261488-2012
  • 财政年份:
    2014
  • 资助金额:
    $ 0.96万
  • 项目类别:
    Discovery Grants Program - Individual
Bayesian Information Criteria and Problems of Parameter Identifiability
贝叶斯信息准则和参数可辨识性问题
  • 批准号:
    1305154
  • 财政年份:
    2013
  • 资助金额:
    $ 0.96万
  • 项目类别:
    Continuing Grant
Developing New Algebraic Geometric Information Criteria for Monte Carlo Inference and Model Selection in Latent Variable and Missing Data Problems
为潜变量和缺失数据问题中的蒙特卡罗推理和模型选择开发新的代数几何信息准则
  • 批准号:
    261488-2012
  • 财政年份:
    2013
  • 资助金额:
    $ 0.96万
  • 项目类别:
    Discovery Grants Program - Individual
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了