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Penalized Clustering of Large-Scale Functional Data With Multiple Covariates

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  • Ma, Ping
  • Zhong, Wenxuan

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  • Ma, Ping & Zhong, Wenxuan, 2008. "Penalized Clustering of Large-Scale Functional Data With Multiple Covariates," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 625-636, June.
    • Handle: RePEc:bes:jnlasa:v:103:y:2008:m:june:p:625-636
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    Cited by:

    1. Beibei Zhang & Rong Chen, 2018. "Nonlinear Time Series Clustering Based on Kolmogorov-Smirnov 2D Statistic," Journal of Classification, Springer;The Classification Society, vol. 35(3), pages 394-421, October.
    2. Ana Arribas-Gil & Rolando De la Cruz & Emilie Lebarbier & Cristian Meza, 2015. "Classification of longitudinal data through a semiparametric mixed-effects model based on lasso-type estimators," Biometrics, The International Biometric Society, vol. 71(2), pages 333-343, June.
    3. Shaikh Mateen & McNicholas Paul D & Desmond Anthony F, 2010. "A Pseudo-EM Algorithm for Clustering Incomplete Longitudinal Data," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-17, March.
    4. Lian, Heng, 2010. "Sparse Bayesian hierarchical modeling of high-dimensional clustering problems," Journal of Multivariate Analysis, Elsevier, vol. 101(7), pages 1728-1737, August.
    5. Cui, Xia & Lin, Hongmei & Lian, Heng, 2020. "Partially functional linear regression in reproducing kernel Hilbert spaces," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).
    6. Li, Pai-Ling & Chiou, Jeng-Min, 2011. "Identifying cluster number for subspace projected functional data clustering," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2090-2103, June.
    7. Wang, Shaoli & Huang, Mian & Wu, Xing & Yao, Weixin, 2016. "Mixture of functional linear models and its application to CO2-GDP functional data," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 1-15.
    8. Lin, Hongmei & Zhang, Riquan & Xu, Wenchao & Wang, Yuedong, 2017. "Estimating time-varying treatment switching effects via local linear smoothing and quasi-likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 50-63.
    9. Vinciotti Veronica & Yu Keming, 2009. "M-quantile Regression Analysis of Temporal Gene Expression Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-20, September.
    10. Xing, Xin & Xie, Rui & Zhong, Wenxuan, 2022. "Model-based sparse coding beyond Gaussian independent model," Computational Statistics & Data Analysis, Elsevier, vol. 166(C).
    11. Coffey, N. & Hinde, J. & Holian, E., 2014. "Clustering longitudinal profiles using P-splines and mixed effects models applied to time-course gene expression data," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 14-29.
    12. Coffey Norma & Hinde John, 2011. "Analyzing Time-Course Microarray Data Using Functional Data Analysis - A Review," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-32, May.
    13. Farcomeni Alessio & Arima Serena, 2012. "A Bayesian autoregressive three-state hidden Markov model for identifying switching monotonic regimes in Microarray time course data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(4), pages 1-31, June.
    14. Zhu, Hanbing & Li, Rui & Zhang, Riquan & Lian, Heng, 2020. "Nonlinear functional canonical correlation analysis via distance covariance," Journal of Multivariate Analysis, Elsevier, vol. 180(C).

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