Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  develop a new robust stopping criterion for partial least squares regression (PLSR) component construction, characterized by a high level of stability. This new criterion is universal since it is suitable both for PLSR and extensions to generalized linear regression (PLSGLR). The criterion is based on a non-parametric bootstrap technique and must be computed algorithmically. It allows the testing of each successive component at a preset significance level \(\alpha \). In order to assess its performance and robustness with respect to various noise levels,  perform dataset simulations in which there is a preset and known number of components. These simulations are carried out for datasets characterized both by \(n>p\), with n the number of subjects and p the number of covariates, as ll as for \(n<p\).  then use t-tests to compare the predictive performance of our approach with other common criteria. The stability property is in particular tested through re-sampling processes on a real allelotyping dataset. An important additional conclusion is that this new criterion gives globally better predictive performances than existing ones in both the PLSR and PLSGLR (logistic and poisson) frameworks.
['Jérémy Magnanensi', 'Frédéric Bertrand', 'Myriam Maumy-Bertrand', 'Nicolas Meyer']
I am writing a research paper on a idea A new universal resample-stable bootstrap-based stopping criterion for PLS component construction
[{"from": "human", "value": "I am writing a research paper on a idea A new universal resample-stable bootstrap-based stopping criterion for PLS component construction"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  develop a new robust stopping criterion for partial least squares regression (PLSR) component construction, characterized by a high level of stability. This new criterion is universal since it is suitable both for PLSR and extensions to generalized linear regression (PLSGLR). The criterion is based on a non-parametric bootstrap technique and must be computed algorithmically. It allows the testing of each successive component at a preset significance level \\(\\alpha \\). In order to assess its performance and robustness with respect to various noise levels,  perform dataset simulations in which there is a preset and known number of components. These simulations are carried out for datasets characterized both by \\(n>p\\), with n the number of subjects and p the number of covariates, as ll as for \\(n<p\\).  then use t-tests to compare the predictive performance of our approach with other common criteria. The stability property is in particular tested through re-sampling processes on a real allelotyping dataset. An important additional conclusion is that this new criterion gives globally better predictive performances than existing ones in both the PLSR and PLSGLR (logistic and poisson) frameworks."}]