@misc{10481/95987,
year = {2024},
month = {10},
url = {https://hdl.handle.net/10481/95987},
abstract = {The availability of many variables with predictive power makes their selection
in a regression context difficult. This study considers robust and understandable
low-dimensional estimators as building blocks to improve overall predictive power
by optimally combining these building blocks. Our new algorithm is based on generalized
cross-validation and builds a predictive model step-by-step from a simple mean
to more complex predictive combinations. Empirical applications to annual financial
returns and actuarial telematics data show its usefulness in the financial and insurance
industries.},
organization = {inisterio de Ciencia, Innovación y Universidades
(PID2020- 116587GB-I00)},
organization = {Austrian National Bank (Jubiläumsfondsprojekt
18901)},
publisher = {Springer},
keywords = {Forecasting},
keywords = {Non-linear prediction},
keywords = {Stock returns},
title = {Robustifying and simplifying high‑dimensional regression with applications to yearly stock return and telematics data},
doi = {10.1186/s40854-024-00657-9},
author = {Marchese, Malvina and Martínez Miranda, María Dolores and Perch Nielsen, Jens and Scholz, Michael},
}