SILFS:Subgroup Identification with Latent Factor Structure
In various domains, many datasets exhibit both high variable dependency and group structures, which necessitates
their simultaneous estimation. This package provides functions
for two subgroup identification methods based on penalized
functions, both of which utilize factor model structures to
adapt to data with cross-sectional dependency. The first method
is the Subgroup Identification with Latent Factor Structure
Method (SILFSM) we proposed. By employing Center-Augmented
Regularization and factor structures, the SILFSM effectively
eliminates data dependencies while identifying subgroups within
datasets. For this model, we offer optimization functions based
on two different methods: Coordinate Descent and our newly
developed Difference of Convex-Alternating Direction Method of
Multipliers (DC-ADMM) algorithms; the latter can be applied to
cases where the distance function in Center-Augmented
Regularization takes L1 and L2 forms. The other method is the
Factor-Adjusted Pairwise Fusion Penalty (FA-PFP) model, which
incorporates factor augmentation into the Pairwise Fusion
Penalty (PFP) developed by Ma, S. and Huang, J. (2017)
<doi:10.1080/01621459.2016.1148039>. Additionally, we provide a
function for the Standard CAR (S-CAR) method, which does not
consider the dependency and is for comparative analysis with
other approaches. Furthermore, functions based on the Bayesian
Information Criterion (BIC) of the SILFSM and the FA-PFP method
are also included in 'SILFS' for selecting tuning parameters.
For more details of Subgroup Identification with Latent Factor
Structure Method, please refer to He et al. (2024)
<doi:10.48550/arXiv.2407.00882>.
