Sum of ranking differences (SRD) to ensemble multivariate calibration model merits for tuning parameter selection and comparing calibration methods.
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Summary
It is shown that the SRD consensus ranking of model tuning parameters allows automatic selection of the final model, or a collection of models if so desired, and is shown to allow simultaneous comparison of different calibration methods for a particular data set in conjunction with tuning parameter selection.
- Type
- dissertation
- Published
- 2015-04-15
- Cited by
- 40
- References
- 63
- Access
- Open access
- OpenAlex
- https://openalex.org/W25818136
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:21106814
Keywords
Pathogenesis, Haplotype, Population, Thrombocytosis, Essential thrombocythemia
References
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- Sums of ranking differences and inversion numbers for method discrimination
- Characterizing multivariate calibration tradeoffs (bias, variance, selectivity, and sensitivity) to select model tuning parameters
- Sum of ranking differences for method discrimination and its validation: comparison of ranks with random numbers
- Chemometrics: A Practical Guide
- Ranking and similarity for quantitative structure-retention relationship models in predicting Lee retention indices of polycyclic aromatic hydrocarbons.
- Theory of analytical chemistry
- Ridge regression optimization using a harmonious approach
- Operationally realistic validation for prediction of cocoa sensory qualities by high-throughput mass spectrometry.
- Comparison of chemometric methods in the analysis of pharmaceuticals with hyperspectral Raman imaging
- Non‐parametric statistical methods for multivariate calibration model selection and comparison
- Desirability function approach: A review and performance evaluation in adverse conditions
- Generalized cross-validation as a method for choosing a good ridge parameter
- Basis sets for multivariate regression
- Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models
- Monte Carlo cross validation
Cited by
- Model selection for partial least squares calibration and implications for analysis of atmospheric organic aerosol samples with mid‐infrared spectroscopy
- Consistency of QSAR models: Correct split of training and test sets, ranking of models and performance parameters†
- Feasibility Study for Transforming Spectral and Instrumental Artifacts for Multivariate Calibration Maintenance
- Boosting in block variable subspaces: An approach of additive modeling for structure–activity relationship
- Structure‐interaction relationship study of N‐(4‐phenylsubstituted) cyanoacetamides by multivariate methods
- Analysis of functional groups in atmospheric aerosols by infrared spectroscopy: sparse methods for statistical selection of relevant absorption bands
- Using the L1 norm to select basis set vectors for multivariate calibration and calibration updating
- Penalty processes for combining roughness and smoothness in spectral multivariate calibration
- Fusion strategies for selecting multiple tuning parameters for multivariate calibration and other penalty based processes: A model updating application for pharmaceutical analysis.
- Recent advances in analytical figures of merit: heteroscedasticity strikes back
- Variable clustering and spectral angle mapper‐orthogonal projection method for Raman mapping of compound detection in tablets
- How important is to detect systematic error in predictions and understand statistical applicability domain of QSAR models
- Sample‐wise spectral multivariate calibration desensitized to new artifacts relative to the calibration data using a residual penalty
- Correction to Consensus Outlier Detection Using Sum of Ranking Differences of Common and New Outlier Measures Without Tuning Parameter Selections.
- Benchmarking of Computational Methods for Creation of Retention Models in Quantitative Structure-Retention Relationships Studies
- Model population analysis in model evaluation
- Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure.
- Post-Pareto Optimality Analysis With Sum of Ranking Differences
- Tuning parameter identification for variable selection algorithm using the sum of ranking differences algorithm
- AdaBoost Ensemble Correction Models for TDDFT Calculated Absorption Energies
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