kernel_reg

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

kernel_reg function

<dl><dt>X</dt>
<dd>A numerical vector representing the sample data of variable X.</dd>
<dt>Y</dt>
<dd>A numerical vector representing the sample data of variable Y.</dd>
<dt>x</dt>
<dd>The point at which the regression function is to be estimated.</dd>
<dt>h</dt>
<dd>A bandwidth parameter that determines the weight assigned to each observation in X.</dd>
<dt>kernel_func</dt>
<dd>A function that computes the weight of each observation based on its distance to x.</dd></dl>

Arguments

Kernel Regression function — kernel_reg

<dl>

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


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


<dt>x</dt>
<dd>The point at which the regression function is to be estimated.</dd>


<dt>h</dt>
<dd>A bandwidth parameter that determines the weight assigned to each observation in X.</dd>


<dt>kernel_func</dt>
<dd>A function that computes the weight of each observation based on its distance to x.</dd>

</dl>

kernel_reg: Kernel Regression function

Description

Usage

Value

Arguments