prasadraoest {smallarea}

Estimate of the variance component in Fay Herriot Model using Prasad Rao Method

Description

This function returns a list with one element in it which is the estimate of the variance component in the Fay Herriot Model. The method used to get the estimate is the prasad rao method also known as BLUP.(for details see vignette). Note that our function does not accept any missing values.

Usage

prasadraoest(response, designmatrix, sampling.var)

Arguments

response

a numeric vector. It represents the response or the observed value in the Fay Herriot Model

designmatrix

a numeric matrix. The first column is a column of ones(also called the intercept). The other columns consist of observations of each of the covariates or the explanatory variable in Fay Herriot Model.

sampling.var

a numeric vector consisting of the known sampling variances of each of the small area levels.

Details

For more details see the package vignette

Value

estimate

estimate of the variance component

Author(s)

Abhishek Nandy

References

On measuring the variability of small area estimators under a basic area level model. Datta, Rao, Smith. Biometrika(2005),92, 1,pp. 183-196 Large Sample Techniques for Statistics, Springer Texts in Statistics. Jiming Jiang. Chapters - 4,12 and 13.

See Also

fayherriot maximlikelihood resimaxilikelihood

Examples

response=c(1,2,3,4,5)
designmatrix=cbind(c(1,1,1,1,1),c(1,2,4,4,1),c(2,1,3,1,5))
randomeffect.var=c(0.5,0.7,0.8,0.4,0.5)
prasadraoest(response,designmatrix,randomeffect.var)