| Name : R-HDMFA
| |
| Version : 0.1.1
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp153.1.4
| Date : 2024-06-14 11:16:31
|
| Group : Development/Libraries/Other
| Source RPM : R-HDMFA-0.1.1-lp153.1.4.src.rpm
|
| Size : 0.07 MB
| |
| Packager : (none)
| |
| Summary : High-Dimensional Matrix Factor Analysis
|
Description :
High-dimensional matrix factor models have drawn much attention in view
of the fact that observations are usually well structured to be an
array such as in macroeconomics and finance. In addition, data often
exhibit heavy-tails and thus it is also important to develop robust
procedures. We aim to address this issue by replacing the least square
loss with Huber loss function. 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 Frobenius norm,
which leads to a weighted iterative projection approach to compute and
learn the parameters and thereby named as Robust-Matrix-Factor-Analysis
(RMFA), see the details in He et al.
(2023)< doi:10.1080/07350015.2023.2191676>. The other one is based on
minimizing the element-wise Huber loss, which can be solved by an
iterative Huber regression algorithm (IHR), see the details in He et
al. (2023) < arXiv:2306.03317>. In this package, we also provide the
algorithm for alpha-PCA by Chen & Fan (2021)
< doi:10.1080/01621459.2021.1970569>, the Projected estimation (PE)
method by Yu et al. (2022)< doi:10.1016/j.jeconom.2021.04.001>. In
addition, the methods for determining the pair of factor numbers are
also given.
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
