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不等间隔的一阶多项式模型及贝叶斯预测 被引量:2
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作者 蒋金凤 《统计与决策》 CSSCI 北大核心 2004年第3期21-22,共2页
关键词 一阶多项式模型 贝叶斯预测 不等间隔 递推关系 折扣模型 状态方程
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Quadratic investigation of geochemical distribution by backward elimination approach at Glojeh epithermal Au(Ag)-polymetallic mineralization, NW Iran
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作者 Darabi-Golestan Farshad Hezarkhani Ardeshir 《Journal of Central South University》 SCIE EI CAS CSCD 2018年第2期342-356,共15页
The correspondence analysis will describe elemental association accompanying an indicator samples.This analysis indicates strong mineralization of Ag,As,Pb,Te,Mo,Au,Zn and to a lesser extent S,W,Cu at Glojeh polymetal... The correspondence analysis will describe elemental association accompanying an indicator samples.This analysis indicates strong mineralization of Ag,As,Pb,Te,Mo,Au,Zn and to a lesser extent S,W,Cu at Glojeh polymetallic mineralization,NW Iran.This work proposes a backward elimination approach(BEA)that quantitatively predicts the Au concentration from main effects(X),quadratic terms(X2)and the first order interaction(Xi×Xj)of Ag,Cu,Pb,and Zn by initialization,order reduction and validation of model.BEA is done based on the quadratic model(QM),and it was eliminated to reduced quadratic model(RQM)by removing insignificant predictors.During the QM optimization process,overall convergence trend of R2,R2(adj)and R2(pred)is obvious,corresponding to increase in the R2(pred)and decrease of R2.The RQM consisted of(threshold value,Cu,Ag×Cu,Pb×Zn,and Ag2-Pb2)and(Pb,Ag×Cu,Ag×Pb,Cu×Zn,Pb×Zn,and Ag2)as main predictors of optimized model according to288and679litho-samples in trenches and boreholes,respectively.Due to the strong genetic effects with Au mineralization,Pb,Ag2,and Ag×Pb are important predictors in boreholes RQM,while the threshold value is known as an important predictor in the trenches model.The RQMs R2(pred)equal74.90%and60.62%which are verified by R2equal to73.9%and60.9%in the trenches and boreholes validation group,respectively. 展开更多
关键词 correspondence analysis first order interaction reduced quadratic model (RQM) optimized model order reduction and validation strong genetic effects
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