silverman_bandwidth

Implements Silverman's rule of thumb for selecting an optimal bandwidth in kernel density estimation.

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

silverman_bandwidth function

<dl><dt>X</dt>
<dd>A numerical vector of sample data.</dd>
<dt>kernel_type</dt>
<dd>A string identifying the kernel type.</dd></dl>

Arguments

Silverman's Rule of Thumb for Bandwidth Selection — silverman_bandwidth

<dl>

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


<dt>kernel_type</dt>
<dd>A string identifying the kernel type.</dd>

</dl>

silverman_bandwidth: Silverman's Rule of Thumb for Bandwidth Selection

Description

Usage

Value

Arguments

Examples