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Uncertainty in Bayesian Leave-One-Out Cross-Validation Based Model Comparison
Bayesian Analysis, 2020Leave-one-out cross-validation (LOO-CV) is a popular method for comparing Bayesian models based on their estimated predictive performance on new, unseen, data.
Tuomas Sivula +3 more
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Cross validation for uncertain autoregressive model
Communications in statistics. Simulation and computation, 2020Uncertain time series models have been investigated to predict future values based on imprecise observations. The existing researches focus on how to estimate unknown parameters in the uncertain time series model without considering how to determine the ...
Zhe Liu, Xiangfeng Yang
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Chemometrics and Intelligent Laboratory Systems, 2018
Abstract Cross validation (CV) is by far one of the most commonly used methods to estimate model complexity for partial least squares (PLS). In this study, stochastic cross validation (SCV) was proposed as a novel CV strategy, where the percent of left-out objects (PLOO) was defined as a changeable random number.
Lu Xu +7 more
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Abstract Cross validation (CV) is by far one of the most commonly used methods to estimate model complexity for partial least squares (PLS). In this study, stochastic cross validation (SCV) was proposed as a novel CV strategy, where the percent of left-out objects (PLOO) was defined as a changeable random number.
Lu Xu +7 more
openaire +1 more source
2023
Validation is one of the most important steps in chemometric modelling, which can drastically change the final results and the performance of a model, especially when it is being applied for the prediction of new data. The best solution for validation of a final model with optimized hyperparameters is to use a validation set – an independent set of ...
Kucheryavskiy, Sergey +2 more
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Validation is one of the most important steps in chemometric modelling, which can drastically change the final results and the performance of a model, especially when it is being applied for the prediction of new data. The best solution for validation of a final model with optimized hyperparameters is to use a validation set – an independent set of ...
Kucheryavskiy, Sergey +2 more
openaire +2 more sources
Model averaging prediction by
Xinyu Zhang, Chu-An Liu
semanticscholar +1 more source
Spatial cross-validation is not the right way to evaluate map accuracy
, 2021A. Wadoux +3 more
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NIR news, 2010
W hen the same test set is used both to tune a calibration algorithm and to give an assessment of its performance, the assessment will be over optimistic. one solution to this problem is to use three sets: calibration, tuning and validation sets. Cross-validation, often used as an alternative to removing a test set, suffers from the same problem of ...
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W hen the same test set is used both to tune a calibration algorithm and to give an assessment of its performance, the assessment will be over optimistic. one solution to this problem is to use three sets: calibration, tuning and validation sets. Cross-validation, often used as an alternative to removing a test set, suffers from the same problem of ...
openaire +1 more source
Cross-validation is safe to use
Nature Machine Intelligence, 2021R. King +2 more
semanticscholar +1 more source
Communications in Statistics - Simulation and Computation, 2015
Data-based choice of the bandwidth is an important problem in kernel density estimation. The pseudo-likelihood and the least-squares cross-validation bandwidth selectors are well known, but widely criticized in the literature. For heavy-tailed distributions, the L1 distance between the pseudo-likelihood-based estimator and the density does not seem to ...
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Data-based choice of the bandwidth is an important problem in kernel density estimation. The pseudo-likelihood and the least-squares cross-validation bandwidth selectors are well known, but widely criticized in the literature. For heavy-tailed distributions, the L1 distance between the pseudo-likelihood-based estimator and the density does not seem to ...
openaire +1 more source