cv.alpha

<code>cv.alpha</code> returns the optimal robustness parameter

Computing singular value decomposition with robustness is a challenging task.
This package provides an implementation of computing robust SVD using density power
divergence (<doi:10.48550/arXiv.2109.10680>). It combines the idea of robustness and efficiency in estimation
based on a tuning parameter. It also provides utility functions to simulate various
scenarios to compare performances of different algorithms.

Subhrajyoty Roy

rsvddpd

Robust Singular Value Decomposition using Density Power
Divergence

cv.alpha function

<dl><dt>X</dt>
<dd><code>matrix</code>, whose singular value decomposition is required</dd>
<dt>alphas</dt>
<dd><code>numeric vector</code>, vector of robustness parameters to try.</dd></dl>

Arguments

Calculate optimal robustness parameter — cv.alpha

<dl>

<dt>X</dt>
<dd><code>matrix</code>, whose singular value decomposition is required</dd>


<dt>alphas</dt>
<dd><code>numeric vector</code>, vector of robustness parameters to try.</dd>

</dl>

cv.alpha: Calculate optimal robustness parameter

Description

Usage

Value

Arguments

References