PLS组件构造中一种新的基于重采样稳定自举的通用停止准则
A new Universal Resample Stable Bootstrap-based Stopping Criterion in PLS Components Construction
摘要：
我们开发了一个新的强大的停止标准，在偏最小二乘回归（PLSR）组件建设的特点是高水平的稳定性。这个新的准则被定义为一个通用的，因为它是既适用于PLSR和它的推广到广义线性回归（PLSGLR）。这个标准是基于一个非参数的自助过程，必须计算算法。它允许在预设的显著性水平α上测试每个连续的分量。为了评估其性能和鲁棒性相对于不同的噪声水平，我们进行密集的数据集模拟，与预设和已知数量的组件提取，无论是在情况n>p（n是受试者的数量和p的原始预测的数量），并与n<p的数据集。然后，我们使用t-检验比较我们的方法的性能与其他一些经典的标准。鲁棒性的属性，特别是通过一个真正的等位基因分型数据集上的restaurant过程进行测试。我们的结论是，我们的标准也提出了更好的全球预测性能，无论是在PLSR和PLSGLR（逻辑和泊松）框架。
英文摘要：
We develop a new robust stopping criterion in Partial Least Squares Regressions (PLSR) components construction characterised by a high level of stability. This new criterion is defined as a universal one since it is suitable both for PLSR and its extension to Generalized Linear Regressions (PLSGLR). This criterion is based on a non-parametric bootstrap process and has to be computed algorithmically. It allows to test each successive components on a preset significant level alpha. In order to assess its performances and robustness with respect to different noise levels, we perform intensive datasets simulations, with a preset and known number of components to extract, both in the case n>p (n being the number of subjects and p the number of original predictors), and for datasets with n<p. We then use t-tests to compare the performance of our approach to some others classical criteria. The property of robustness is particularly tested through resampling processes on a real allelotyping dataset. Our conclusion is that our criterion presents also better global predictive performances, both in the PLSR and PLSGLR (Logistic and Poisson) frameworks.
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。
