tr_estimate

For a design matrix \(\mathbf{X}\), estimate the trace of its covariance matrix \(\Sigma = \mathrm{cov}(\mathbf{X})\),
and the square of covariance matrix \(\Sigma^2\).

Semi-distance and mean-variance (MV) index are proposed to measure the dependence between a categorical random variable and a continuous variable.
Test of independence and feature screening for classification problems can be implemented via the two dependence measures.
For the details of the methods, see Zhong et al. (2023) <doi:10.1080/01621459.2023.2284988>;
Cui and Zhong (2019) <doi:10.1016/j.csda.2019.05.004>;
Cui, Li and Zhong (2015) <doi:10.1080/01621459.2014.920256>.

Zhuoxi Li

semidist

Measure Dependence Between Categorical and Continuous Variables

Wei Zhong

Wenwen Guo

Hengjian Cui

Runze Li

tr_estimate function

<dl><dt>X</dt>
<dd>The design matrix.</dd></dl>

Arguments

Estimate the trace of the covariance matrix and its square — tr_estimate

<dl>

<dt>X</dt>
<dd>The design matrix.</dd>

</dl>

tr_estimate: Estimate the trace of the covariance matrix and its square

Description

Usage

Value

Arguments