Estimation of varies types of estimators in the linear model

Description

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.

Details

Package: lrmest
Type: Package
Version: 3.0
Date: 2016-05-13
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.

Author(s)

P.Wijekoon, A.Dissanayake

Maintainer: Ajith Dissanayake <sudeera333@gmail.com>

References

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)

Liu, K. (1993) A new class of biased estimate in linear regression in Communications in Statistics-Theory and Methods 22, pp. 393–402

Nagler, J. (Updated 2011) Notes on Ordinary Least Square Estimators

Theil, H. and Goldberger, A.S. (1961) On pure and mixed statistical estimation in economics in International Economic review 2, pp. 65–78

Revan, M. (2009) A stochastic restricted ridge regression estimator in Journal of Multivariate Analysis, volume 100, issue 8, pp. 1706–1716

Rong,Jian-Ying (2010) Adjustive Liu Type Estimators in linear regression models in communication in statistics-simulation and computation, volume 39 DOI:10.1080/03610918.2010.484120

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

See Also

optimum, pcd

Examples

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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.

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