rm_theta_new_pred

RM_theta_new_pred

Traditional EBLUP Estimator of Theta for new <code>X</code> values

Implements the Agnostic Fay-Herriot model, an extension of
the traditional small area model. In place of normal sampling errors, the
sampling error distribution is estimated with a Gaussian process to
accommodate a broader class of distributions. This flexibility is most
useful in the presence of bounded, multi-modal, or heavily skewed sampling
errors.

Marten Thompson

agfh

Agnostic Fay-Herriot Model for Small Area Statistics

Snigdhansu Chatterjee

RM_theta_new_pred function

<dl> <dt>X.new</dt>
<dd>new independent data to be analyzed</dd> <dt>beta.est</dt>
<dd>estimate of regression term for latent model</dd></dl>

Arguments

Traditional EBLUP Estimator of Theta for new X values — RM_theta_new_pred

<dl>

 <dt>X.new</dt>
<dd>new independent data to be analyzed</dd>

 <dt>beta.est</dt>
<dd>estimate of regression term for latent model</dd>

</dl>

RM_theta_new_pred: Traditional EBLUP Estimator of Theta for new `X` values

Description

Usage

Value

Arguments

Details

Examples