On the use of cross-validation to assess performance in multivariate prediction
On the use of cross-validation to assess performance in multivariate prediction
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DOI：
10.1023/a:1008987426876
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发表时间：
2000-07-01
影响因子：
2.2
通讯作者：
McCarthy, WV
中科院分区：
文献类型：
--
作者：
Jonathan, P;Krzanowski, WJ;McCarthy, WV
我们描述了一个Monte Carlo调查的交叉验证的预测模型的性能评估，包括不同值的k在留k交叉验证，并实施无论是在一个深或两个深的时尚的一些变种。我们假设使用岭回归或偏最小二乘法拟合的潜在线性模型，并改变一些设计因素，例如相对于变量p的样本量n和误差方差。研究包括非奇异（即n > p）和奇异（即n小于或等于p）情况。后者现在在化学计量学等领域很常见，但尚未得到严格的研究。实验结果使我们能够得出一些明确的结论，并提出一些实际的建议。
We describe a Monte Carlo investigation of a number of variants of cross-validation for the assessment of performance of predictive models, including different values of k in leave-k-out cross-validation, and implementation either in a one-deep or a two-deep fashion. We assume an underlying linear model that is being fitted using either ridge regression or partial least squares, and vary a number of design factors such as sample size n relative to number of variables p, and error variance. The investigation encompasses both the non-singular (i.e. n > p) and the singular (i.e. n less than or equal to p) cases. The latter is now common in areas such as chemometrics but has as yet received little rigorous investigation. Results of the experiments enable us to reach some definite conclusions and to make some practical recommendations.