单纯形分布广义线性模型的贝叶斯变量选择
단순형분포엄의선성모형적패협사변량선택
Bayesian Subset Selection for Simplex Distributi on Linear Models
  • 万方数据
    内蒙古师范大学学报（自然科学汉文版） 내몽고사범대학학보（자연과학한문판）
    JOURNAL OF INNER MONGOLIA NORMAL UNIVERSITY(NATURAL SCIENCE EDITION)
    2015年 4期 421-424 ,共4页

    赵远英%徐登可%段星德%戴亮

    조원영%서등가%단성덕%대량

    贝叶斯变量选择%Gibbs 抽样%MH 算法%单纯形分布广义线性模型 貝葉斯變量選擇%Gibbs 抽樣%MH 算法%單純形分佈廣義線性模型
    패협사변량선택%Gibbs 추양%MH 산법%단순형분포엄의선성모형
    Bayesian subset selection%Gibbs sampler%Metropolis-Hastings algorithm%simplex distri-bution linear models
    应用 Gibbs 抽样和 MH 算法研究单纯形分布广义线性模型的贝叶斯变量选择问题。定义了包含模型不确定性的单纯形分布广义线性模型，在该模型框架下描述了能有效识别预测变量的变量选择方法。最后用数值例子说明了该方法的有效性。
    응용 Gibbs 추양화 MH 산법연구단순형분포엄의선성모형적패협사변량선택문제。정의료포함모형불학정성적단순형분포엄의선성모형，재해모형광가하묘술료능유효식별예측변량적변량선택방법。최후용수치례자설명료해방법적유효성。
    Bayesian subset selection for simplex distribution linear models is studied by Gibbs sampler and Metropolis-Hastings algorithm in this paper.Firstly,we define simplex distribution linear models which include model uncertainty;And then we explicitly describe a MCMC procedure that can identify the promising subsets of predictors under the simplex distribution linear models.Numerical examples are used to illustrate the proposed methodology.
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