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A joint design for functional data with application to scheduling ultrasound scans

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  • Park, So Young
  • Xiao, Luo
  • Willbur, Jayson D.
  • Staicu, Ana-Maria
  • Jumbe, N. L’ntshotsholé

Abstract

A joint design for sampling functional data is proposed to achieve optimal prediction of both functional data and a scalar outcome. The motivating application is fetal growth, where the objective is to determine the optimal times to collect ultrasound measurements in order to recover fetal growth trajectories and to predict child birth outcomes. The joint design is formulated using an optimization criterion and implemented in a pilot study. Performance of the proposed design is evaluated via simulation study and application to fetal ultrasound data.

Suggested Citation

  • Park, So Young & Xiao, Luo & Willbur, Jayson D. & Staicu, Ana-Maria & Jumbe, N. L’ntshotsholé, 2018. "A joint design for functional data with application to scheduling ultrasound scans," Computational Statistics & Data Analysis, Elsevier, vol. 122(C), pages 101-114.
    • Handle: RePEc:eee:csdana:v:122:y:2018:i:c:p:101-114
    DOI: 10.1016/j.csda.2018.01.009
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    References listed on IDEAS

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    1. Haocheng Li & John Staudenmayer & Raymond J. Carroll, 2014. "Hierarchical functional data with mixed continuous and binary measurements," Biometrics, The International Biometric Society, vol. 70(4), pages 802-811, December.
    2. A. Delaigle & P. Hall & N. Bathia, 2012. "Componentwise classification and clustering of functional data," Biometrika, Biometrika Trust, vol. 99(2), pages 299-313.
    3. Morris, Jeffrey S. & Arroyo, Cassandra & Coull, Brent A. & Ryan, Louise M. & Herrick, Richard & Gortmaker, Steven L., 2006. "Using Wavelet-Based Functional Mixed Models to Characterize Population Heterogeneity in Accelerometer Profiles: A Case Study," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1352-1364, December.
    4. Hao Ji & Hans-Georg Müller, 2017. "Optimal designs for longitudinal and functional data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 859-876, June.
    5. Philip T. Reiss & R. Todd Ogden, 2010. "Functional Generalized Linear Models with Images as Predictors," Biometrics, The International Biometric Society, vol. 66(1), pages 61-69, March.
    6. F. Ferraty & P. Hall & P. Vieu, 2010. "Most-predictive design points for functional data predictors," Biometrika, Biometrika Trust, vol. 97(4), pages 807-824.
    7. 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.
    8. Martin A. Lindquist, 2012. "Functional Causal Mediation Analysis With an Application to Brain Connectivity," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1297-1309, December.
    9. Jeff Goldsmith & Tomoko Kitago, 2016. "Assessing systematic effects of stroke on motor control by using hierarchical function-on-scalar regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(2), pages 215-236, February.
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    Cited by:

    1. Craig, Sarah J.C. & Kenney, Ana M. & Lin, Junli & Paul, Ian M. & Birch, Leann L. & Savage, Jennifer S. & Marini, Michele E. & Chiaromonte, Francesca & Reimherr, Matthew L. & Makova, Kateryna D., 2023. "Constructing a polygenic risk score for childhood obesity using functional data analysis," Econometrics and Statistics, Elsevier, vol. 25(C), pages 66-86.
    2. Rha, Hyungmin & Kao, Ming-Hung & Pan, Rong, 2020. "Design optimal sampling plans for functional regression models," Computational Statistics & Data Analysis, Elsevier, vol. 146(C).

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