saeME (version 1.3.1)

dataME: dataME

Description

This data generated by simulation based on Fay-Herriot with Measurement Error Model by following these steps:

1. Generate \(x_{i}\) from a UNIF(5, 10) distribution, \(\psi_{i}\) = 3, \(c_{i}\) = 0.25, and \(\sigma_{v}^{2}\) = 2.

2. Generate \(u_{i}\) from a N(0, \(c_{i}\)) distribution, \(e_{i}\) from a N(0, \(\psi_{i}\)) distribution, and \(v_{i}\) from a N(0, \(\sigma_{v}^{2}\)) distribution.

3. Generate \(\hat{x}_{i}\) = \(x_{i}\) + \(u_{i}\).

4. Then for each iteration, we generated \(Y_{i}\) = \(2 + 0.5 \hat{x}_{i} + v_{i}\) and \(y_{i}\) = \(Y_{i} + e_{i}\).

Direct estimator y, auxiliary variable \(\hat{x}\), sampling variance \(\psi\), and \(c\) are arranged in a dataframe called dataME.

Usage

data(dataME)

Format

A data frame with 100 observations on the following 4 variables.

small_area

areas of interest.

y

direct estimator for each domain.

x.hat

auxiliary variable for each domain.

vardir

sampling variances for each domain.

var.x

mean squared error of auxiliary variable and sorted as x.hat