Estimate of the variance component in Fay Herriot Model using Residual Maximum Likelihood, REML.

Description

This function returns a list with one element in it which is the estimate of the variance component in the Fay Herriot Model using residual maximum likelihood method. The estimates are obtained as a solution of equations known as REML equations. The solution is obtained numerically using Fisher-scoring algorithm. For more details please see the package vignette and the references. Note that our function does not accept any missing values.

Usage

1
resimaxilikelihood(response, designmatrix, sampling.var,maxiter)

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.

maxiter

maximum number of iterations of fisher scoring

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. Small Area Estimation, JNK Rao,Wiley 2003 Variance Components, Wiley Series in Probability and Statistics,2006 Searle, Casella, Mc-Culloh

See Also

prasadraoest maximlikelihood fayherriot

Examples

1
2
3
4
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)
resimaxilikelihood(response,designmatrix,randomeffect.var,100)