Evaluation and selection of models for out-of-sample prediction when the sample size is small relative to the complexity of the data-generating process
| dc.creator | Leeb, Hannes | |
| dc.date | 2008-02-22 | |
| dc.date | 2008-10-24 | |
| dc.date.accessioned | 2026-07-07T10:12:34Z | |
| dc.date.available | 2026-07-07T10:12:34Z | |
| dc.description | In regression with random design, we study the problem of selecting a model that performs well for out-of-sample prediction. We do not assume that any of the candidate models under consideration are correct. Our analysis is based on explicit finite-sample results. Our main findings differ from those of other analyses that are based on traditional large-sample limit approximations because we consider a situation where the sample size is small relative to the complexity of the data-generating process, in the sense that the number of parameters in a `good' model is of the same order as sample size. Also, we allow for the case where the number of candidate models is (much) larger than sample size. | |
| dc.description | Published in at http://dx.doi.org/10.3150/08-BEJ127 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm) | |
| dc.identifier | https://arxiv.org/abs/0802.3364 | |
| dc.identifier | http://arxiv.org/abs/0802.3364 | |
| dc.identifier | Bernoulli 2008, Vol. 14, No. 3, 661-690 | |
| dc.identifier | doi:10.3150/08-BEJ127 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/172223 | |
| dc.subject | Methodology | |
| dc.subject | Statistics Theory | |
| dc.title | Evaluation and selection of models for out-of-sample prediction when the sample size is small relative to the complexity of the data-generating process | |
| dc.type | text |