RT Journal Article
T1 A general framework for prediction in penalized regression
A1 Carballo González, Alba
A1 Durbán Reguera, María Luz
A1 Kauermann, Goran
A1 Lee, Dae-Jin
AB There are two main approaches to carrying out prediction in the context of penalized regression: with low-rank basis and penalties or through the smooth mixed models. In this article, we give further insight in the case of P-splines showing the influence of the penalty on the prediction. In the context of mixed models, we can connect the new predicted values to the observed values through a joint normal distribution, which allows us to compute prediction intervals. In this work, we propose an alternative approach, called the extended mixed model approach, that allows us to fit and predict data simultaneously. The methodology is illustrated with two real datasets, one of them on aboveground biomass and the other on monthly sulphur dioxide (SO2) levels in a selection of monitoring sites in Europe.
PB SAGE Publishing
SN 1471-082X
YR 2020
FD 2020-02-27
LK https://hdl.handle.net/10016/32234
UL https://hdl.handle.net/10016/32234
LA eng
NO The first and second authors acknowledge financial support from the Spanish Ministry of Economy and Competitiveness MTM2014-52184-P. The fourth author acknowledges the financial support by the Basque Government through the BERC 2018-2021 programme and by Spanish Ministry of Economy and Competitiveness MINECO through BCAM Severo Ochoa excellence accreditation SEV-2013-0323 and through project MTM2017-82379-R funded by (AEI/FEDER, UE) and acronym AFTERAM.
DS e-Archivo
RD 1 sept. 2024