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Multiclass sparse logistic regression for classification of multiple cancer types using gene expression data

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  • Kim, Yongdai
  • Kwon, Sunghoon
  • Heun Song, Seuck

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  • Kim, Yongdai & Kwon, Sunghoon & Heun Song, Seuck, 2006. "Multiclass sparse logistic regression for classification of multiple cancer types using gene expression data," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1643-1655, December.
    • Handle: RePEc:eee:csdana:v:51:y:2006:i:3:p:1643-1655
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    References listed on IDEAS

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    1. Dudoit S. & Fridlyand J. & Speed T. P, 2002. "Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 77-87, March.
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    Cited by:

    1. Liu, Shen & Maharaj, Elizabeth Ann, 2013. "A hypothesis test using bias-adjusted AR estimators for classifying time series in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 32-49.
    2. He, Huizi & Sun, Mei & Li, Xiuming & Mensah, Isaac Adjei, 2022. "A novel crude oil price trend prediction method: Machine learning classification algorithm based on multi-modal data features," Energy, Elsevier, vol. 244(PA).
    3. Vincent, Martin & Hansen, Niels Richard, 2014. "Sparse group lasso and high dimensional multinomial classification," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 771-786.
    4. Lee, Sangin & Kwon, Sunghoon & Kim, Yongdai, 2016. "A modified local quadratic approximation algorithm for penalized optimization problems," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 275-286.
    5. Liu, Shen & Maharaj, Elizabeth Ann & Inder, Brett, 2014. "Polarization of forecast densities: A new approach to time series classification," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 345-361.
    6. Douzal-Chouakria, Ahlame & Diallo, Alpha & Giroud, Françoise, 2009. "Adaptive clustering for time series: Application for identifying cell cycle expressed genes," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1414-1426, February.
    7. Tian, Guo-Liang & Tang, Man-Lai & Fang, Hong-Bin & Tan, Ming, 2008. "Efficient methods for estimating constrained parameters with applications to regularized (lasso) logistic regression," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3528-3542, March.
    8. Colubi, Ana & González-Rodri­guez, Gil & Domi­nguez-Cuesta, Mari­a José & Jiménez-Sánchez, Montserrat, 2008. "Favorability functions based on kernel density estimation for logistic models: A case study," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4533-4543, May.

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