ogsrliu: Ordinary Generalized Stochastic Restricted Liu Estimator

Description

This function can be used to find the Ordinary Generalized Stochastic Restricted Liu Estimated values and corresponding scalar Mean Square Error (MSE) value. Further the variation of MSE can be shown graphically.

Usage

ogsrliu(formula, r, R, dpn, delt, d, data = NULL, na.action, ...)

Arguments

formula

in this section interested model should be given. This should be given as a formula.

is a $j$ by $1$ matrix of linear restriction, $r = R\beta + \delta + \nu$. Values for r should be given as either a vector or a matrix. See

is a $j$ by $p$ of full row rank $j \le p$ matrix of linear restriction, $r = R\beta + \delta + \nu$. Values for R should be given as either a vector or a matrix. See Examples.

dpn

dispersion matrix of vector of disturbances of linear restricted model, $r = R\beta + \delta + \nu$. Values for dpn should be given as either a vector (only the diagonal elements) or a matrix. See Examples

delt

values of $E(r) - R\beta$ and that should be given as either a vector or a matrix. See Examples.

a single numeric value or a vector of set of numeric values. See Example.

data

an optional data frame, list or environment containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which the function is called.

na.action

if the dataset contain NA values, then na.action indicate what should happen to those NA values.

...

currently disregarded.

Value

If d is a single numeric values then ogsrliu returns the Ordinary Generalized Stochastic Restricted Liu Estimated values, standard error values, t statistic values, p value and corresponding scalar MSE value. If d is a vector of set of numeric values then ogsrliu returns all the scalar MSE values and corresponding parameter values of Ordinary Generalized Stochastic Resticted Liu Estimator.

Details

Since formula has an implied intercept term, use either y ~ x - 1 or y ~ 0 + x to remove the intercept. Use plot so as to obtain the variation of scalar MSE values graphically. See Examples.

References

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)

Examples

Run this code

## Portland cement data set is used.
data(pcd)
d<-0.05
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)
ogsrliu(Y~X1+X2+X3+X4-1,r,R,dpn,delt,d,data=pcd)    
 # Model without the intercept is considered.

## To obtain the variation of MSE of Ordinary Generalized Stochastic 
# Restricted Liu Estimator.
data(pcd)
d<-c(0:10/10)
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)
plot(ogsrliu(Y~X1+X2+X3+X4-1,r,R,dpn,delt,d,data=pcd),
main=c("Plot of MSE of Ordinary Generalized Stochastic Restricted Liu 
Estimator"),type="b",cex.lab=0.6,adj=1,cex.axis=0.6,cex.main=1,las=1,lty=3,cex=0.6)
mseval<-data.frame(ogsrliu(Y~X1+X2+X3+X4-1,r,R,dpn,delt,d,data=pcd))
smse<-mseval[order(mseval[,2]),]
points(smse[1,],pch=16,cex=0.6)

Run the code above in your browser using DataLab

Last chance! 50% off unlimited learning