estimate.unknownsampvar: Estimates of variance component, unknown sampling variance,...
In nandy006/smallarea: Fits a Fay Herriot Model

Description Usage Arguments Details Value Author(s) References See Also Examples

The function returns a list of 5 elements. The first element is an estimate of the variance component , the second element is an estimate of the parameter related to sampling variance, the third element is a vector of estimates of the regression coefficients in the Fay-Herriot model, the fourth element is a vector of the predictors pf the small area means and last element is the design matrix, the first column being a column of ones and the remaining columns represent the values of the covariates for different small areas. See details below.

1	estimate.unknownsampvar(response, mat.design, sample.size)

`response`	A numeric vector. It represents the response or the direct survey based estimators in the Fay Herriot Model
`mat.design`	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.
`sample.size`	A numeric vector. The known sample sizes used to calculate the direct survey based estimators.

For more details please see the package vignette.

`psi.hat`	Estimate of the variance component
`del.hat`	Estimate of the parameter for sampling variance
`beta.hat`	Estimate of the unknown regression coefficients
`theta.hat`	Predictors of the small area means
`mat.design`	design matrix

Abhishek Nandy

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.

prasadraoest fayherriot

set.seed( 55 )                  # setting a random seed
require(MASS)                   # the function mvrnorm requires MASS
x1 <- rep( 1, 10 )              # vector of ones representing intercept
x2 <- rnorm( 10 )               # vector of covariates randomly generated
x <- cbind( x1, x2 )            # design matrix
x <- as.matrix( x )             # coercing into class matrix
n <- rbinom (10, 20, 0.4)       # generating sample sizes for each small area
psi <- 1                        # true value of the psi parameter
delta <- 1                      # true value of the delta parameter
beta <- c( 1, 0.5 )             # true values of the regression parameters
theta <- mvrnorm( 1, as.vector( x %*% beta ), diag(10) )  # true small area means
y <- mvrnorm( 1, as.vector( theta ), diag( delta/n ) )  # design based estimators
estimate.unknownsampvar( y, x, n )