相对于数据生成过程的复杂性而言，样本量较小时样本外预测模型的评价和选择
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
摘要：
在随机设计的回归中，我们研究了选择一个模型的问题，该模型在样本外预测中表现良好。我们并不假设任何候选模型都是正确的。我们的分析是基于明确的有限样本结果。我们的主要研究结果不同于那些基于传统的大样本极限近似的其他分析，因为我们考虑的情况下，样本量相对于数据生成过程的复杂性是小的，在这个意义上说，在一个“好”的模型中的参数的数量是相同的顺序作为样本量。此外，我们允许候选模型的数量（远）大于样本量的情况。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
