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Consensus Outlier Detection Using Sum of Ranking Differences of Common and New Outlier Measures Without Tuning Parameter Selections

https://doi.org/10.1021/acs.analchem.7b00637
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Robust Regression and Outlier Detection
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Learning from Procrustes analysis to improve multivariate calibration
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Sum of ranking differences for method discrimination and its validation: comparison of ranks with random numbers
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Evaluation of target factor analysis and net analyte signal as processes for classification purposes with application to benchmark data sets and extra virgin olive oil adulterant identification
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A new strategy of outlier detection for QSAR/QSPR
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Application of near infrared reflectance spectroscopy to the compositional analysis of biscuits and biscuit doughs
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Identification of Outliers
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Matrix Correlation
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Estimation of influential points in any data set from coefficient of determination and its leave-one-out cross-validated counterpart
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Related versions of the multiplicative scatter correction method for preprocessing spectroscopic data
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Extended multiplicative signal correction and spectral interference subtraction: New preprocessing methods for near infrared spectroscopy
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Locally centred Mahalanobis distance: A new distance measure with salient features towards outlier detection
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Sum of ranking differences (SRD) to ensemble multivariate calibration model merits for tuning parameter selection and comparing calibration methods
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Fusion strategies for selecting multiple tuning parameters for multivariate calibration and other penalty based processes: A model updating application for pharmaceutical analysis
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Procrustes rotation in analytical chemistry, a tutorial
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Method and model comparison by sum of ranking differences in cases of repeated observations (ties)
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Improvement on enhanced Monte-Carlo outlier detection method
inferredcites →https://doi.org/10.1016/j.trac.2009.09.009openalex-snapshot
Sum of ranking differences compares methods or models fairly
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Model-population analysis and its applications in chemical and biological modeling
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Multiple outlier detection for multivariate calibration using robust statistical techniques
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Multiple outlier detection revisited
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Individualization of Gasoline Samples by Covariance Mapping and Gas Chromatography/Mass Spectrometry
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Orthogonal Projection Approach Applied to Peak Purity Assessment
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Outlier Detection in Multivariate Analytical Chemical Data
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An Adaptive, Automatic Multiple-Case Deletion Technique for Detecting Influence in Regression
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Least Median of Squares Regression
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Unmasking Multivariate Outliers and Leverage Points
inferredcites →https://doi.org/10.1080/01621459.1999.10474199openalex-snapshot
The Masking Breakdown Point of Multivariate Outlier Identification Rules
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Feature Subset Selection in Remote Sensing
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A Comparison of Multivariate Outlier Detection Methods for Clinical Laboratory Safety Data
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A user-friendly guide to Multivariate Calibration and Classification
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Removing Outliers
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Near-Infrared Absorption and Scattering Separated by Extended Inverted Signal Correction (EISC): Analysis of Near-Infrared Transmittance Spectra of Single Wheat Seeds
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Linearization and Scatter-Correction for Near-Infrared Reflectance Spectra of Meat
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Relations between Mid-Infrared and Near-Infrared Spectra Detected by Analysis of Variance of an Intervariable Data Matrix
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Fundamentals of Calibration Transfer through Procrustes Analysis
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Feasibility Study for Transforming Spectral and Instrumental Artifacts for Multivariate Calibration Maintenance

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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