RT Journal Article
T1 Out-of-sample prediction in multidimensional P-spline models
A1 Carballo González, Alba
A1 Durbán Reguera, María Luz
A1 Lee, Dae-Jin
AB The prediction of out-of-sample values is an interesting problem in any regression model. In the context of penalized smoothing using a mixed-model reparameterization, a general framework has been proposed for predicting in additive models but without interaction terms. The aim of this paper is to generalize this work, extending the methodology proposed in the multidimensional case, to models that include interaction terms, i.e., when prediction is carried out in a multidimensional setting. Our method fits the data, predicts new observations at the same time, and uses constraints to ensure a consistent fit or impose further restrictions on predictions. We have also developed this method for the so-called smooth-ANOVA model, which allows us to include interaction terms that can be decomposed into the sum of several smooth functions. We also develop this methodology for the so-called smooth-ANOVA models, which allow us to include interaction terms that can be decomposed as a sum of several smooth functions. To illustrate the method, two real data sets were used, one for predicting the mortality of the U.S. population in a logarithmic scale, and the other for predicting the aboveground biomass of Populus trees as a smooth function of height and diameter. We examine the performance of interaction and the smooth-ANOVA model through simulation studies.
PB MDPI AG
SN 2227-7390
YR 2021
FD 2021-08-01
LK https://hdl.handle.net/10016/34404
UL https://hdl.handle.net/10016/34404
LA eng
NO This research was funded in part by Ministerio de Ciencia e Innovación grant numbersPID2019-104901RB-I00. The third author gratefully acknowledges support by the Departmentof Education, Language Policy and Culture from the Basque Government (BERC 2018-2021 program),the Spanish Ministry of Economy and Competitiveness MINECO and FEDER: PID2020-115882RB-I00/AEI/10.13039/501100011033 funded by Agencia Estatal de Investigación and acronym“S3M1P4R”, and BCAM Severo Ochoa excellence accreditation SEV-2017-0718).
DS e-Archivo
RD 27 jul. 2024