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Joint estimation of monotone curves via functional principal component analysis
Cited 2 time in
Web of Science
Cited 3 time in Scopus
- Authors
- Issue Date
- 2022-02
- Publisher
- Elsevier BV
- Citation
- Computational Statistics and Data Analysis, Vol.166, p. 107343
- 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. (C) 2021 Published by Elsevier B.V.
- ISSN
- 0167-9473
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