get_est_Ar

This function estimates the parameters A and r by optimizing an objective function
over a specified range of frequency values and r values.

A novel bias-bound approach for non-parametric inference is introduced,
focusing on both density and conditional expectation estimation.
It constructs valid confidence intervals that account for the presence of
a non-negligible bias and thus make it possible to perform inference with
optimal mean squared error minimizing bandwidths.
This package is based on Schennach (2020) <doi:10.1093/restud/rdz065>.

Xinyu DAI

rbbnp

A Bias Bound Approach to Non-Parametric Inference

Susanne M Schennach

get_est_Ar function

<dl><dt>Y</dt>
<dd>A numerical vector representing the sample data of variable Y.</dd>
<dt>X</dt>
<dd>A numerical vector representing the sample data of variable X.</dd>
<dt>xi_interval</dt>
<dd>A list with elements <code>xi_lb</code> and <code>xi_ub</code> representing the lower
and upper bounds of the frequency interval.</dd>
<dt>r_stepsize</dt>
<dd>An integer value representing the number of steps in the r range.
This controls the granularity of the estimation. Higher values lead to finer
granularity but increase computation time.</dd></dl>

Arguments

get the estimation of A and r — get_est_Ar

<dl>

<dt>Y</dt>
<dd>A numerical vector representing the sample data of variable Y.</dd>


<dt>X</dt>
<dd>A numerical vector representing the sample data of variable X.</dd>


<dt>xi_interval</dt>
<dd>A list with elements <code>xi_lb</code> and <code>xi_ub</code> representing the lower
and upper bounds of the frequency interval.</dd>


<dt>r_stepsize</dt>
<dd>An integer value representing the number of steps in the r range.
This controls the granularity of the estimation. Higher values lead to finer
granularity but increase computation time.</dd>

</dl>

get_est_Ar: get the estimation of A and r

Description

Usage

Value

Arguments

Details