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由于高维问题所研究的数据量往往比较大，而样本容量相对不多，故其渐近性质的讨论与传统的大样本性质分析有一定的区别。随着对高维问题研究的深入，一些不可观测的大样本问题逐渐出现，如重复构造的数据结构、采用再抽样（resampling）方法提取数据等等。这类问题引发的思考是：原始问题并非大样本，因模型转换和参数估计过程中产生的大样本问题，其渐近性质应如何考虑？
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