H‐methods in applied sciences

@article{Hskuldsson2008HmethodsIA,
  title={H‐methods in applied sciences},
  author={A. H{\"o}skuldsson},
  journal={Journal of Chemometrics},
  year={2008},
  volume={22}
}
The author has developed a framework for mathematical modelling within applied sciences. It is characteristic for data from ‘nature and industry’ that they have reduced rank for inference. It means that full rank solutions normally do not give satisfactory solutions. The basic idea of H‐methods is to build up the mathematical model in steps by using weighing schemes. Each weighing scheme produces a score and/or a loading vector that are expected to perform a certain task. Optimisation… Expand
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14 Citations
Highly Influential Citations
1
Background Citations
4
Methods Citations
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14 Citations

Citation Type

Citation Type

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Improved variable reduction in partial least squares modelling based on predictive-property-ranked variables and adaptation of partial least squares complexity.
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Predictive-property-ranked variable reduction in partial least squares modelling with final complexity adapted models: comparison of properties for ranking.
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Predictive-property-ranked variable reduction with final complexity adapted models in partial least squares modeling for multiple responses.
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Practical Considerations on Nonparametric Methods for Estimating Intrinsic Dimensions of Nonlinear Data Structures
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Improved variable reduction in partial least squares modelling by Global-Minimum Error Uninformative-Variable Elimination.
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Improvement of quantitative structure-retention relationship models for chromatographic retention prediction of peptides applying individual local partial least squares models.
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