Transfer of Multivariate Calibration Models

Brown, Steven D.

doi:10.1016/b978-0-12-409547-2.00644-2

Cited by 5 publications

(2 citation statements)

References 144 publications

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“…Model corrections become necessary when measurement ( secondary ) conditions (sample (molecule), instrument, and environment matrix effects) for new secondary samples become sufficiently different from the original ( primary ) calibration conditions. Because conditions have changed, the original model does not provide accurate property values for secondary samples …”

Section: Introductionmentioning

confidence: 99%

Reliable Model Selection without Reference Values by Utilizing Model Diversity with Prediction Similarity

Spiers

Kalivas

2021

J. Chem. Inf. Model.

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Predictive modeling (calibration or training) with various data formats, such as near-infrared (NIR) spectra and quantitative structure−activity relationship (QSAR) data, provides essential information if a proper model is selected. Similarly, with a general model selection approach, spectral model maintenance (updating) from original modeling conditions to new conditions can be performed for dynamic modeling. Fundamental modeling (partial least-squares (PLS) and others) and maintenance processes (domain adaptation or transfer learning and others) require selection of tuning parameter(s) values to isolate models that can accurately predict new samples or molecules, e.g., number of PLS latent variables to predict analyte concentration. Regardless of the modeling task, model selection is complex and without a reliable protocol. Tuning parameter selection typically depends on only one model quality measure assessing model bias using prediction accuracy. Developed in this paper is a generic model selection process using concepts from consensus modeling and QSAR activity landscapes. It is a consensus filtering approach that prioritizes model diversity (MD) while conserving prediction similarity (PS) fused with a common bias-variance trade-off measure. A significant feature of MDPS is that a cross-validation scheme is not needed because models are selected relative to predicting new samples or molecules, i.e., model selection uses unlabeled samples (without reference values) for active predictions. The versatility and reliability of MDPS model selection is shown using four NIR data sets and a QSAR data set. The study also substantiates the Rashomon effect where there is not one best model tuning parameter value that provides accurate predictions.

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Section: Introductionmentioning

confidence: 99%

Reliable Model Selection without Reference Values by Utilizing Model Diversity with Prediction Similarity

Spiers

Kalivas

2021

J. Chem. Inf. Model.

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“…In epidemiology, various studies may be cited where the impact of an exposure to noxious substances on the health status of people is studied (e.g., Heid et al, 2002). In engineering, the calibration of measuring instruments deals with measurement errors by definition (Brown, 1982). Many more examples can be found in the literature, in particular in the monographs by Schneeweiss and Mittag (1986), Fuller (1987), Carroll et al (1995), Cheng and Van Ness (1999), Wansbeek and Meijer (2000).…”

Section: Introductionmentioning

confidence: 99%

Some Recent Advances in Measurement Error Models and Methods

Schneeweiß

Augustin

Modern Econometric Analysis

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A measurement error model is a regression model with (substantial) measurement errors in the variables. Disregarding these measurement errors in estimating the regression parameters results in asymptotically biased estimators. Several methods have been proposed to eliminate, or at least to reduce, this bias, and the relative efficiency and robustness of these methods have been compared. The paper gives an account of these endeavors. In another context, when data are of a categorical nature, classification errors play a similar role as measurement errors in continuous data. The paper also reviews some recent advances in this field.

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