estimate_EY_X_squared

This function estimates the squared conditional expectation E[(E[Y|X])^2] using
Nadaraya-Watson regression with Gaussian kernel.

internal

Provides tools to compute the Generalized Measure of Correlation (GMC),
a dependence measure accounting for nonlinearity and asymmetry in the relationship
between variables. Based on the method proposed by Zheng, Shi, and Zhang (2012) <doi:10.1080/01621459.2012.710509>.

Xuejing Ding

Generalized Measure of Correlation (GMC)

Zhengjun Zhang

estimate_EY_X_squared function

<dl><dt>X</dt>
<dd>A numeric vector of predictors.</dd>
<dt>Y</dt>
<dd>A numeric vector of responses.</dd>
<dt>grid_length</dt>
<dd>Number of grid points for numerical integration (default = 10000).</dd>
<dt>kernel</dt>
<dd>Kernel function (default is dnorm).</dd></dl>

Arguments

Estimate E[(E[Y|X])^2] using kernel regression — estimate_EY_X_squared

<dl>

<dt>X</dt>
<dd>A numeric vector of predictors.</dd>


<dt>Y</dt>
<dd>A numeric vector of responses.</dd>


<dt>grid_length</dt>
<dd>Number of grid points for numerical integration (default = 10000).</dd>


<dt>kernel</dt>
<dd>Kernel function (default is dnorm).</dd>

</dl>

estimate_EY_X_squared: Estimate E[(E[Y|X])^2] using kernel regression

Description

Usage

Value

Arguments

References