Bootstrap confidence intervals for trilinear partial least squares regression
| Autor: | Sven Serneels, Pierre J. Van Espen |
|---|---|
| Rok vydání: | 2005 |
| Předmět: |
Chemistry
Estimator Expression (computer science) Biochemistry Confidence interval Robust confidence intervals Analytical Chemistry Parameter identification problem Partial least squares regression Statistics Confidence distribution Environmental Chemistry Spectroscopy CDF-based nonparametric confidence interval |
| Zdroj: | Analytica chimica acta |
| ISSN: | 0003-2670 |
| DOI: | 10.1016/j.aca.2005.02.012 |
| Popis: | The boostrap is a successful technique to obtain confidence limits for estimates where it is theoretically impossible to establish an exact expression thereunto. Trilinear partial least squares regression (tri-PLS) is an estimator for which this is the case; in the current paper we thus propose to apply the bootstrap in order to obtain confidence intervals for the predictions made by tri-PLS. By dint of an extensive simulation study, we show that bootstrap confidence intervals have a desirable coverage. Finally, we apply the method to an identification problem of micro-organisms and show that from the bootstrap confidence intervals, the organisms can (up to a misclassification probability of 3.5%) correctly be identified. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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