شماره ركورد كنفرانس :
4330
عنوان مقاله :
Jackknife-After-Bootstrap in Fuzzy Regression Modeling
عنوان به زبان ديگر :
Jackknife-After-Bootstrap in Fuzzy Regression Modeling
پديدآورندگان :
Kashani .M kashani.mo jtaba@yahoo.com Shahrood University of Technology , Arashi .M m_arash_stat@yahoo.com Shahrood University of Technology , Rabiei .M.R rabie1354@yahoo.com Shahrood University of Technology
كليدواژه :
Bootstrap , Fuzzy least squares regression , Fuzzy regression , Jackknife.
عنوان كنفرانس :
هفدهمين كنفررانس ملي سيستم هاي فازي، پانزدهمين كنفرانس ملي سيستم هاي هوشمند و ششمين كنگره ملي مشترك سيستم هاي فازي و هوشمند ايران
چكيده فارسي :
There are many endeavors to exhibit a fuzzy regression model which can, in a
logical sense, improve goodness of fit (GOF) and power of prediction. Considering different well-known approaches of fuzzy regression modeling (FRM), including possibility, least squares and other combined methods, we propose a method to improve the GOF and prediction in FRM. Specifically, we introduce a resampling procedure to improve the estimation of the regression coefficients. Numerical studies demonstrate the superior performance of the proposed resampling procedure over usual ones..
چكيده لاتين :
There are many endeavors to exhibit a fuzzy regression model which can, in a
logical sense, improve goodness of fit (GOF) and power of prediction. Considering different well-known approaches of fuzzy regression modeling (FRM), including possibility, least squares and other combined methods, we propose a method to improve the GOF and prediction in FRM. Specifically, we introduce a resampling procedure to improve the estimation of the regression coefficients. Numerical studies demonstrate the superior performance of the proposed resampling procedure over usual ones..
