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Shrinkage Estimation for Functional Principal Component Scores with Application to the Population Kinetics of Plasma Folate

Citations

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Cited by:

  1. Park, Juhyun & Gasser, Theo & Rousson, Valentin, 2009. "Structural components in functional data," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3452-3465, July.
  2. Tomáš Rubín & Victor M. Panaretos, 2020. "Functional lagged regression with sparse noisy observations," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(6), pages 858-882, November.
  3. Liebl, Dominik, 2010. "Modeling hourly Electricity Spot Market Prices as non stationary functional times series," MPRA Paper 25017, University Library of Munich, Germany.
  4. Cederbaum, Jona & Scheipl, Fabian & Greven, Sonja, 2018. "Fast symmetric additive covariance smoothing," Computational Statistics & Data Analysis, Elsevier, vol. 120(C), pages 25-41.
  5. Chiou, Jeng-Min & Muller, Hans-Georg, 2007. "Diagnostics for functional regression via residual processes," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4849-4863, June.
  6. Hans-Georg Müller & Ying Zhang, 2005. "Time-Varying Functional Regression for Predicting Remaining Lifetime Distributions from Longitudinal Trajectories," Biometrics, The International Biometric Society, vol. 61(4), pages 1064-1075, December.
  7. Beran, Jan & Liu, Haiyan, 2016. "Estimation of eigenvalues, eigenvectors and scores in FDA models with dependent errors," Journal of Multivariate Analysis, Elsevier, vol. 147(C), pages 218-233.
  8. Peter Hall & Hans‐Georg Müller & Fang Yao, 2008. "Modelling sparse generalized longitudinal observations with latent Gaussian processes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 703-723, September.
  9. 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.
  10. Jaeeun Yu & Jinsu Park & Taeryon Choi & Masahiro Hashizume & Yoonhee Kim & Yasushi Honda & Yeonseung Chung, 2021. "Nonparametric Bayesian Functional Meta-Regression: Applications in Environmental Epidemiology," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(1), pages 45-70, March.
  11. Jimin Ding & Jane-Ling Wang, 2008. "Modeling Longitudinal Data with Nonparametric Multiplicative Random Effects Jointly with Survival Data," Biometrics, The International Biometric Society, vol. 64(2), pages 546-556, June.
  12. Chao, Shih-Kang & Härdle, Wolfgang Karl & Huang, Chen, 2016. "Multivariate factorisable sparse asymmetric least squares regression," SFB 649 Discussion Papers 2016-058, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
  13. Müller, Hans-Georg & Sen, Rituparna & Stadtmüller, Ulrich, 2011. "Functional data analysis for volatility," Journal of Econometrics, Elsevier, vol. 165(2), pages 233-245.
  14. Usset, Joseph & Staicu, Ana-Maria & Maity, Arnab, 2016. "Interaction models for functional regression," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 317-329.
  15. Li, Yingxing & Härdle, Wolfgang Karl & Huang, Chen, 2017. "Smooth principal component analysis for high dimensional data," SFB 649 Discussion Papers 2017-024, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
  16. Caleb Weaver & Luo Xiao & Wenbin Lu, 2023. "Functional data analysis for longitudinal data with informative observation times," Biometrics, The International Biometric Society, vol. 79(2), pages 722-733, June.
  17. Haochang Shou & Vadim Zipunnikov & Ciprian M. Crainiceanu & Sonja Greven, 2015. "Structured functional principal component analysis," Biometrics, The International Biometric Society, vol. 71(1), pages 247-257, March.
  18. Rha, Hyungmin & Kao, Ming-Hung & Pan, Rong, 2020. "Design optimal sampling plans for functional regression models," Computational Statistics & Data Analysis, Elsevier, vol. 146(C).
  19. 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.
  20. repec:hum:wpaper:sfb649dp2016-058 is not listed on IDEAS
  21. Yao, Fang, 2007. "Asymptotic distributions of nonparametric regression estimators for longitudinal or functional data," Journal of Multivariate Analysis, Elsevier, vol. 98(1), pages 40-56, January.
  22. J. Goldsmith & S. Greven & C. Crainiceanu, 2013. "Corrected Confidence Bands for Functional Data Using Principal Components," Biometrics, The International Biometric Society, vol. 69(1), pages 41-51, March.
  23. Şentürk, Damla & Ghosh, Samiran & Nguyen, Danh V., 2014. "Exploratory time varying lagged regression: Modeling association of cognitive and functional trajectories with expected clinic visits in older adults," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 1-15.
  24. Orlando Joaqui-Barandica & Diego F. Manotas-Duque, 2023. "How do Climate and Macroeconomic Factors Affect the Profitability of the Energy Sector?," International Journal of Energy Economics and Policy, Econjournals, vol. 13(4), pages 444-454, July.
  25. repec:hum:wpaper:sfb649dp2017-024 is not listed on IDEAS
  26. Li, Yingxing & Huang, Chen & Härdle, Wolfgang K., 2019. "Spatial functional principal component analysis with applications to brain image data," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 263-274.
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