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Recovering Gradients from Sparsely Observed Functional Data

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  • Sara López-Pintado
  • Ian W. McKeague

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  • Sara López-Pintado & Ian W. McKeague, 2013. "Recovering Gradients from Sparsely Observed Functional Data," Biometrics, The International Biometric Society, vol. 69(2), pages 396-404, June.
    • Handle: RePEc:bla:biomet:v:69:y:2013:i:2:p:396-404
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    File URL: http://hdl.handle.net/10.1111/biom.12011
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    References listed on IDEAS

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    1. Liu, Bitao & Müller, Hans-Georg, 2009. "Estimating Derivatives for Samples of Sparsely Observed Functions, With Application to Online Auction Dynamics," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 704-717.
    2. Peter Hall & Mohammad Hosseini‐Nasab, 2006. "On properties of functional principal components analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 109-126, February.
    3. Yao, Fang & Muller, Hans-Georg & Wang, Jane-Ling, 2005. "Functional Data Analysis for Sparse Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 577-590, June.
    4. Takao Shohoji & Kouji Kanefuji & Takahiro Sumiya & Tao Qin, 1991. "A prediction of individual growth of height according to an empirical Bayesian approach," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(4), pages 607-619, December.
    5. Cai, Tony & Liu, Weidong & Luo, Xi, 2011. "A Constrained â„“1 Minimization Approach to Sparse Precision Matrix Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 594-607.
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