get_est_byx

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_byx 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>...</dt>
<dd>Additional arguments passed to other methods.</dd></dl>

Arguments

Estimation of bias byx — get_est_byx

<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>...</dt>
<dd>Additional arguments passed to other methods.</dd>

</dl>

get_est_byx: Estimation of bias byx

Description

Usage

Value

Arguments