To combat multicollinearity several estimators have been introduced. By using this package some of those estimators and corresponding scalar Mean Square Error (MSE) values and Prediction Sum of Square (PRESS) values (Only for some estimators) can be found easily. In addition graphical methods are available to determine the variation of MSE values of those estimators and the variation of PRESS values of some of the estimators.
|License:||GPL-2 | GPL-3|
In this package functions have been written for several types of estimators in the linear model. By using those functions relevant estimators can be found.
Maintainer: Ajith Dissanayake <email@example.com>
Akdeniz, F. and Erol, H. (2003) Mean Squared Error Matrix Comparisons of Some Biased Estimators in Linear Regression in Communications in Statistics - Theory and Methods, volume 32 DOI:10.1081/STA-120025385
Arumairajan, S. and Wijekoon, P. (2015) ] Optimal Generalized Biased Estimator in Linear Regression Model in Open Journal of Statistics, pp. 403–411
Hubert, M.H. and Wijekoon, P. (2006) Improvement of the Liu estimator in the linear regression medel, Chapter (4-8)
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Nagler, J. (Updated 2011) Notes on Ordinary Least Square Estimators
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Sarkara, N. (1992), A new estimator combining the ridge regression and the restricted least squares methods of estimation in Communications in Statistics - Theory and Methods, volume 21, pp. 1987–2000. DOI:10.1080/03610929208830893
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## Portland cement dataset is used. data(pcd) attach(pcd) k<-c(0:3/10) d<-c(-3:3/10) r<-c(2.1930,1.1533,0.75850) R<-c(1,0,0,0,0,1,0,0,0,0,1,0) dpn<-c(0.0439,0.0029,0.0325) delt<-c(0,0,0) aa1<-c(0.958451,1.021155,0.857821,1.040296) aa2<-c(0.345454,1.387888,0.866466,1.354454) aa3<-c(0.344841,1.344723,0.318451,1.523316) optimum(Y~X1+X2+X3+X4-1,r,R,dpn,delt,aa1,aa2,aa3,k,d,data=pcd) # Model without the intercept is considered. ## Use "press=TRUE" to get the optimum PRESS values only for some of # the estimators.