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cff-version: 1.2.0
message: 'To cite package "HDRFA" in publications use:'
type: software
license:
- GPL-2.0-only
- GPL-3.0-only
title: 'HDRFA: High-Dimensional Robust Factor Analysis'
version: 0.1.5
doi: 10.32614/CRAN.package.HDRFA
abstract: Factor models have been widely applied in areas such as economics and finance,
  and the well-known heavy-tailedness of macroeconomic/financial data should be taken
  into account when conducting factor analysis. We propose two algorithms to do robust
  factor analysis by considering the Huber loss. One is based on minimizing the Huber
  loss of the idiosyncratic error's L2 norm, which turns out to do Principal Component
  Analysis (PCA) on the weighted sample covariance matrix and thereby named as Huber
  PCA. The other one is based on minimizing the element-wise Huber loss, which can
  be solved by an iterative Huber regression algorithm. In this package we also provide
  the code for traditional PCA, the Robust Two Step (RTS) method by He et al. (2022)
  and the Quantile Factor Analysis (QFA) method by Chen et al. (2021) and He et al.
  (2023).
authors:
- family-names: He
  given-names: Yong
- family-names: Li
  given-names: Lingxiao
- family-names: Liu
  given-names: Dong
  email: liudong_stat@163.com
- family-names: Zhou
  given-names: Wenxin
repository: https://ldstat.r-universe.dev
commit: eb0e8c9844db647b070998d8dbdcc42b0761f04a
date-released: '2024-07-23'
contact:
- family-names: Liu
  given-names: Dong
  email: liudong_stat@163.com