Data_saekernel

Dataset to Simulate Small Area Estimation Non-Parametric Based Nadaraya-Watson Kernel
This data is generated by these following steps:<ol>
<li>Generate explanatory variables <code>Vardir</code>. <code>Vardir ~ abs(N(0, 0.1))</code>
 Generate explanatory variables <code>x</code>. <code>x ~ U(min=0, max=1)</code>
 Calculate direct estimation <code>y</code> where \(y_{i}\) = \(sin(2 * \pi * x^3) + 5\)</li>
<li>Then combine the direct estimations <code>y</code>, auxiliary variables <code>x</code>, and sampling varians <code>Vardir</code> in a dataframe then named as Data_saekernel</li>
</ol>

datasets

Propose an area-level, non-parametric regression estimator based on Nadaraya-Watson kernel on small area mean. Adopt a two-stage estimation approach proposed by Prasad and Rao (1990). Mean Squared Error (MSE) estimators are not readily available, so resampling method that called bootstrap is applied. This package are based on the model proposed in Two stage non-parametric approach for small area estimation by Pushpal Mukhopadhyay and Tapabrata Maiti(2004) <http://www.asasrms.org/Proceedings/y2004/files/Jsm2004-000737.pdf>.

Wicak Hasani

saekernel

Small Area Estimation Non-Parametric Based Nadaraya-Watson
Kernel

Data_saekernel function

A data frame with 100 rows and 3 variables:<dl class="dl-horizontal">
 <dt>y</dt><dd>Direct Estimation of Y</dd>
 <dt>x</dt><dd>Auxiliary Variable of X</dd>
 <dt>Vardir</dt><dd>Sampling Variance of Y</dd>
</dl>

Format

Sample Data for Small Area Estimation Non-Parametric Based Nadaraya-Watson Kernel — Data_saekernel

A data frame with 100 rows and 3 variables:<dl class='dl-horizontal'>
 <dt>y</dt><dd>Direct Estimation of Y</dd>
 <dt>x</dt><dd>Auxiliary Variable of X</dd>
 <dt>Vardir</dt><dd>Sampling Variance of Y</dd>
</dl>

Data_saekernel: Sample Data for Small Area Estimation Non-Parametric Based Nadaraya-Watson Kernel

Description

Usage

Arguments

Format