Putting formal specifications under the magnifying glass: Model-based testing for validation [PDF]
, 2009 A software development process is effectively an abstract form of model transformation, starting from an end-user model of requirements, through to a system model for which code can be automatically generated.
Mark Utting +12 more
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[Responding to children's emotional and cognitive needs: Applying the cumulative model to the general population]. [PDF]
Can J Psychiatry, 2022 Bandola C, Clément MÈ, Bérubé A.
europepmc +1 more source
[Contribution of a hospital pharmacy team to critical care of patients infected with SARS-CoV-2]. [PDF]
Ann Pharm Fr, 2021 Besson C +7 more
europepmc +1 more source
CROSS-VALIDATION ADJUSTMENT FOR MODEL SELECTION WITH CORRELATED DATA
, 2021 In the context of general linear models, often techniques are used with an independence assumption. Unfortunately, this assumption often does not hold in real data.
Adeeb, Ebrahim
core
Model-driven description and validation of composite learning content [PDF]
, 2010 Authoring of learning content for courseware systems is a complex activity requiring the combination of a range of design and validation techniques. We introduce the CAVIAr courseware models allowing for learning content description and validation. Model-
Pahl, Claus, Melia, Mark
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CVTresh: R Package for Level-Dependent Cross-Validation Thresholding [PDF]
The core of the wavelet approach to nonparametric regression is thresholding of wavelet coefficients. This paper reviews a cross-validation method for the selection of the thresholding value in wavelet shrinkage of Oh, Kim, and Lee (2006), and introduces
Hee-Seok Oh, Donghoh Kim
core
Soil Moisture Product Validation Good Practices Protocol
, 2020 Calibration, Land Product Validation Subgroup (Working Group on +2 more
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archipelago-validation-59d10e4.tar
, 2015 archipelago-validation-59d10e4 ...
Sukumaran, Jeet +5 more
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Statistical validation of simulation models: A case study [PDF]
Rigorous statistical validation requires that the responses of the model and the real system have the same expected values. However, the modeled and actual responses are not comparable if they are obtained under different scenarios (environmental ...
Kleijnen, J.P.C.
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No unbiased Estimator of the Variance of K-Fold Cross-Validation [PDF]
In statistical machine learning, the standard measure of accuracy for models is the prediction error, i.e. the expected loss on future examples. When the data distribution is unknown, it cannot be computed but several resampling methods, such as K-fold ...
Yoshua Bengio, Yves Grandvalet
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