Principal Component Analysis for Factor Models

A robust alternative to the traditional principal component estimator is proposed within the framework of factor models, known as Robust Exponential Factor Analysis, specifically designed for the modeling of high-dimensional datasets with heavy-tailed distributions. The algorithm estimates the latent factors and the loading by minimizing the exponential squared loss function. To determine the appropriate number of factors, we propose a modified rank minimization technique, which has been shown to significantly enhance finite-sample performance. For more detail of Robust Exponential Factor Analysis, please refer to Hu et al. (2026) <doi:10.1016/j.jmva.2025.105567>.

Jiaqi Hu

REFA

Robust Exponential Factor Analysis

Xueqin Wang

FA function

<dl><dt>X</dt>
<dd>Input matrix, of dimension \(T\times N\). Each row is an observation with \(N\) features at time point \(t\).</dd>
<dt>r</dt>
<dd>A positive integer indicating the factor numbers.</dd></dl>

Arguments

Author

Principal Component Analysis for Factor Models — FA

<dl>

<dt>X</dt>
<dd>Input matrix, of dimension \(T\times N\). Each row is an observation with \(N\) features at time point \(t\).</dd>


<dt>r</dt>
<dd>A positive integer indicating the factor numbers.</dd>

</dl>

FA: Principal Component Analysis for Factor Models

Description

Usage

Value

Arguments

Author

Examples