epanechnikov_kernel

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

epanechnikov_kernel function

<dl><dt>u</dt>
<dd>A numerical value or vector representing the input to the kernel function.</dd></dl>

Arguments

Epanechnikov Kernel — epanechnikov_kernel

<dl>

<dt>u</dt>
<dd>A numerical value or vector representing the input to the kernel function.</dd>

</dl>

epanechnikov_kernel: Epanechnikov Kernel

Description

Usage

Value

Arguments