File:Data-Driven-Estimation-of-Imputation-Error—A-Strategy-for-Imputation-with-a-Reject-Option-pone.0164464.s003.ogv
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[edit]DescriptionData-Driven-Estimation-of-Imputation-Error—A-Strategy-for-Imputation-with-a-Reject-Option-pone.0164464.s003.ogv |
English: Effect of Threshold, Crocs dataset, Anatomical bias. Movie showing the effect of different thresholds (see Fig 1). A-C. Visualization of the effect of threshold using the complete cases with simulated missing values for A. Probabilistic PCA (PPCA) imputation, B. Mean imputation, C. KNN imputation. In all three, blue represent complete cases. Simulated cases with missing that would be rejected (red) or accepted (green) for the current threshold. The size represents the actual imputation error. D. Threshold versus an errors examplified with Root Mean Square Errors (RMSE) of the actual imputation error for the included cases (full red lines) and the excludedcases (dotted blue lines). |
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Source | S1 Video from Bak N, Hansen L (2016). "Data Driven Estimation of Imputation Error—A Strategy for Imputation with a Reject Option". PLOS ONE. DOI:10.1371/journal.pone.0164464. PMID 27723782. PMC: 5056679. | ||
Author | Bak N, Hansen L | ||
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This file is licensed under the Creative Commons Attribution 4.0 International license.
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Date/Time | Thumbnail | Dimensions | User | Comment | |
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current | 03:16, 29 October 2016 | 20 s, 1,600 × 830 (885 KB) | Open Access Media Importer Bot (talk | contribs) | Automatically uploaded media file from Open Access source. Please report problems or suggestions here. |
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Short title | Effect of Threshold, Crocs dataset, Anatomical bias. |
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Author | Bak N, Hansen L |
Usage terms | http://creativecommons.org/licenses/by/4.0/ |
Image title | Movie showing the effect of different thresholds (see Fig 1). A-C. Visualization of the effect of threshold using the complete cases with simulated missing values for A. Probabilistic PCA (PPCA) imputation, B. Mean imputation, C. KNN imputation. In all three, blue represent complete cases. Simulated cases with missing that would be rejected (red) or accepted (green) for the current threshold. The size represents the actual imputation error. D. Threshold versus an errors examplified with Root Mean Square Errors (RMSE) of the actual imputation error for the included cases (full red lines) and the excludedcases (dotted blue lines). |
Software used | Xiph.Org libtheora 1.1 20090822 (Thusnelda) |
Date and time of digitizing | 2016 |