The sparse principal component can obtain sparse solutions of the eigenmatrix to better explain the relationship between principal components and original variables.

Provides a distributed framework for simulating and estimating skew factor models under various skewed and heavy-tailed distributions. The methods support distributed data generation, aggregation of local estimators, and evaluation of estimation performance via mean squared error, relative error, and sparsity measures. The distributed principal component (PC) estimators implemented in the package include 'IPC' (Independent Principal Component),'PPC' (Project Principal Component), 'SPC' (Sparse Principal Component), and other related distributed PC methods. The methodological background follows Guo G. (2023) <doi:10.1007/s00180-022-01270-z>.

Guangbao Guo

DSFM

Distributed Skew Factor Model Estimation Methods

Yu Jin

SPC function

<dl><dt>data</dt>
<dd>is a highly correlated data set</dd>
<dt>m</dt>
<dd>is the number of principal component</dd>
<dt>gamma</dt>
<dd>is a sparse parameter</dd></dl>

Arguments

The sparse principal component can obtain sparse solutions of the eigenmatrix to better explain the relationship between principal components and original variables. — SPC

<dl>

<dt>data</dt>
<dd>is a highly correlated data set</dd>


<dt>m</dt>
<dd>is the number of principal component</dd>


<dt>gamma</dt>
<dd>is a sparse parameter</dd>

</dl>

The sparse principal component can obtain sparse solutions of the eigenmatrix to better explain the relationship between principal components and original variables.

SPC: The sparse principal component can obtain sparse solutions of the eigenmatrix to better explain the relationship between principal components and original variables.

Description

Usage

Value

Arguments