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Joint estimation of monotone curves via functional principal component analysis

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  • Shin, Yei Eun
  • Zhou, Lan
  • Ding, Yu

Abstract

A functional data approach is developed to jointly estimate a collection of monotone curves that are irregularly and possibly sparsely observed with noise. In this approach, the unconstrained relative curvature curves instead of the monotone-constrained functions are directly modeled. Functional principal components are used to describe the major modes of variations of curves and allow borrowing strength across curves for improved estimation. A two-step approach and an integrated approach are considered for model fitting. The simulation study shows that the integrated approach is more efficient than separate curve estimation and the two-step approach. The integrated approach also provides more interpretable principle component functions in an application of estimating weekly wind power curves of a wind turbine.

Suggested Citation

  • Shin, Yei Eun & Zhou, Lan & Ding, Yu, 2022. "Joint estimation of monotone curves via functional principal component analysis," Computational Statistics & Data Analysis, Elsevier, vol. 166(C).
    • Handle: RePEc:eee:csdana:v:166:y:2022:i:c:s0167947321001778
    DOI: 10.1016/j.csda.2021.107343
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    References listed on IDEAS

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