Ordinary Generalized Type (2) Adjusted Liu Estimator

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Description

This function can be used to find the Ordinary Generalized Type (2) Adjusted Liu Estimated values, corresponding scalar Mean Square Error (MSE) in the linear model. Further the variation of MSE values can be shown graphically.

Usage

1
ogalt2(formula, k, d, aa, data = NULL, na.action, ...)

Arguments

formula

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

k

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

d

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

aa

this is a set of scalars belongs to real number system. Values for “aa” should be given as a vector, format. See ‘Details’.

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.

Details

Since formula has an implied intercept term, use either y ~ x - 1 or y ~ 0 + x to remove the intercept.

In order to get the best results, optimal values for k,d and aa should be selected.

The way of finding aa can be determined from Rong,Jian-Ying (2010) Adjustive Liu Type Estimators in linear regression models in communication in statistics-simulation and computation, volume 39

Use matplot so as to obtain the variation of scalar MSE values graphically. See ‘Examples’.

Value

If k and d are single numeric values then ogalt2 returns the Ordinary Generalized Type (2) Adjusted Liu Estimated values, standard error values, t statistic values, p value, corresponding scalar MSE value.

If k and d are vector of set of numeric values then ogalt2 returns the matrix of scalar MSE values of Ordinary Generalized Type (2) Adjusted Liu Estimator by representing k and d as column names and row names respectively.

Author(s)

P.Wijekoon, A.Dissanayake

References

Arumairajan, S. and Wijekoon, P. (2015) ] Optimal Generalized Biased Estimator in Linear Regression Model in Open Journal of Statistics, pp. 403–411

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

See Also

matplot

Examples

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## Portland cement data set is used.
data(pcd)
k<-0.1650
d<--0.1300
aa<-c(0.958451,1.021155,0.857821,1.040296)
ogalt2(Y~X1+X2+X3+X4-1,k,d,aa,data=pcd)     
# Model without the intercept is considered.

 ## To obtain the variation of MSE of Ordinary Generalized 
 # Type (2) Adjusted Liu Estimator.
data(pcd)
k<-c(0:5/10)
d<-c(390:430/10)
aa<-c(0.958451,1.021155,0.857821,1.040296)
msemat<-ogalt2(Y~X1+X2+X3+X4-1,k,d,aa,data=pcd)
matplot(d,ogalt2(Y~X1+X2+X3+X4-1,k,d,aa,data=pcd),type="l",ylab=c("MSE"),
main=c("Plot of MSE of Ordinary Generalized Type (2) Adjusted 
Liu Estimator"),cex.lab=0.6,adj=1,cex.axis=0.6,cex.main=1,las=1,lty=3)
text(y=msemat[1,],x=d[1],labels=c(paste0("k=",k)),pos=4,cex=0.6)

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