datasaeu: Data generated based on Univariate Fay Herriot Model with...

In sae.prop: Small Area Estimation using Fay-Herriot Models with Additive Logistic Transformation

Data generated based on Univariate Fay Herriot Model with Additive Logistic Transformation

Description

This data is generated based on univariate Fay-Herriot model and then transformed by using inverse Additive Logistic Transformation (alr). The steps are as follows:

1. \beta are set to be \beta_{0} = \beta_{1} = \beta_{2} = 1

2. Auxiliary variables are set as follows:

   • x_{1} \sim N(0, 1)

   • x_{2} \sim N(0.5, 1)

3. For random effects, u \sim N(0, V_{u}), where V_{u} = 1.

4. For sampling errors e \sim N(0, V_{ed}), where V_{ed} is generated V_{ed} \sim InvGamma(50, 0.5).

5. The generated data is transformed using inverse alr transformation, so the data will be within the range of proportion.

Auxiliary variables x_{1}, x_{2}, direct estimation y, and sampling variance vardir are combined into a data frame called datasaeu.

Usage

datasaeu

Format

A data frame with 30 rows and 4 columns:

y

Direct Estimation of y

x1

Auxiliary variable of x1

x2

Auxiliary variable of x2

vardir

Sampling Variance of y

sae.prop documentation built on Oct. 15, 2023, 5:06 p.m.