mspe_PMS_FH_SUMCA

Post model selection MSPE estimation in FH model using SUMCA method. Calculate the post-model selection mspe of Fay-Herriot model using SUMCA method.

Estimation of mean squared prediction error of a small area predictor is provided. In particular, the recent method of Simple, Unified, Monte-Carlo Assisted approach for the mean squared prediction error estimation of small area predictor is provided. We also provide other existing methods of mean squared prediction error estimation such as jackknife method for the mixed logistic model.

Mahmoud Torabi

SumcaVer1

Mean Square Prediction Error Estimation in Small Area Estimation

Jiming Jiang

mspe_PMS_FH_SUMCA function

<dl><dt>m</dt>
<dd>number of small areas</dd>
<dt>p</dt>
<dd>number of fixed model parameters</dd>
<dt>X</dt>
<dd>covariates</dd>
<dt>beta</dt>
<dd>regression coefficients</dd>
<dt>A</dt>
<dd>variance of area-specific random effects</dd>
<dt>D</dt>
<dd>sampling variance</dd>
<dt>K</dt>
<dd>number of Monte Carlo for the SUMCA method</dd>
<dt>R</dt>
<dd>number of simulation runs</dd></dl>

Arguments

Post model selection MSPE estimation in FH model using SUMCA method. Calculate the post-model selection mspe of Fay-Herriot model using SUMCA method. — mspe_PMS_FH_SUMCA

<dl>

<dt>m</dt>
<dd>number of small areas</dd>


<dt>p</dt>
<dd>number of fixed model parameters</dd>


<dt>X</dt>
<dd>covariates</dd>


<dt>beta</dt>
<dd>regression coefficients</dd>


<dt>A</dt>
<dd>variance of area-specific random effects</dd>


<dt>D</dt>
<dd>sampling variance</dd>


<dt>K</dt>
<dd>number of Monte Carlo for the SUMCA method</dd>


<dt>R</dt>
<dd>number of simulation runs</dd>

</dl>

mspe_PMS_FH_SUMCA: Post model selection MSPE estimation in FH model using SUMCA method. Calculate the post-model selection mspe of Fay-Herriot model using SUMCA method.

Description

Usage

Value

Arguments

Examples