Label‐noise resistant logistic regression for functional data classification with an application to Alzheimer's disease study
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DOI: 10.1111/biom.12504
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References listed on IDEAS
- She, Yiyuan & Owen, Art B., 2011. "Outlier Detection Using Nonconvex Penalized Regression," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 626-639.
- Shin, Hyejin, 2008. "An extension of Fisher's discriminant analysis for stochastic processes," Journal of Multivariate Analysis, Elsevier, vol. 99(6), pages 1191-1216, July.
- Lin, Yi, 2004. "A note on margin-based loss functions in classification," Statistics & Probability Letters, Elsevier, vol. 68(1), pages 73-82, June.
- de Leeuw, Jan, 2006. "Principal component analysis of binary data by iterated singular value decomposition," Computational Statistics & Data Analysis, Elsevier, vol. 50(1), pages 21-39, January.
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Cited by:
- Yang, Seong J. & Shin, Hyejin & Lee, Sang Han & Lee, Seokho, 2020. "Functional linear regression model with randomly censored data: Predicting conversion time to Alzheimer ’s disease," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).
- Samami, Maryam & Akbari, Ebrahim & Abdar, Moloud & Plawiak, Pawel & Nematzadeh, Hossein & Basiri, Mohammad Ehsan & Makarenkov, Vladimir, 2020. "A mixed solution-based high agreement filtering method for class noise detection in binary classification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
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