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

Details 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 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

2008-02-22

arxiv.org/abs/0802.3364v2

Bernoulli 2008, Vol. 14, No. 3, 661-690

Statistics

Methodology

Published in at http://dx.doi.org/10.3150/08-BEJ127 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statisti

Scientific paper

10.3150/08-BEJ127

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.

Affiliated with

Also associated with

No associations

Rating

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 does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with 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, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and 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 will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-303581