TY  - JOUR
A1  - Pfitzinger, Johann
T1  - Cluster regularization via a hierarchical feature regression
T2  - Econometrics and statistics
N2  - The hierarchical feature regression (HFR) is a novel graph-based regularized regression estimator, which mobilizes insights from the domains of machine learning and graph theory to estimate robust parameters for a linear regression. The estimator constructs a supervised feature graph that decomposes parameters along its edges, adjusting first for common variation and successively incorporating idiosyncratic patterns into the fitting process. The graph structure has the effect of shrinking parameters towards group targets, where the extent of shrinkage is governed by a hyperparameter, and group compositions as well as shrinkage targets are determined endogenously. The method offers rich resources for the visual exploration of the latent effect structure in the data, and demonstrates good predictive accuracy and versatility when compared to a panel of commonly used regularization techniques across a range of empirical and simulated regression tasks.
KW  - Regularized regression
KW  - Group shrinkage
KW  - Machine learning
KW  - Supervised hierarchical clustering
Y1  - 2024
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/82856
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-828567
SN  - 2452-3062
VL  - 2024
IS  - In Press, Corrected Proof
PB  - Elsevier
CY  - Amsterdam
ER  -