Joint Estimation of Monotone Curves via Functional Principal Component Analysis. Academic Article

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.

authors

• Ding, Yu
• Zhou, Lan

published proceedings

• Comput Stat Data Anal

author list (cited authors)

• Shin, Y. E., Zhou, L., & Ding, Y. u.

citation count

• 1

complete list of authors

• Shin, Yei Eun||Zhou, Lan||Ding, Yu

publication date

• January 2022

publisher

• Elsevier  Publisher

published in

• Computational Statistics and Data Analysis  Journal

keywords

• B-splines
• Functional Data Analysis
• Monotone Smoothing
• Penalization
• Relative Curvature Function
• Spline Smoothing

Digital Object Identifier (DOI)

• 10.1016/j.csda.2021.107343

start page

• 107343

end page

• 107343

volume

• 166

URL

• http://dx.doi.org/10.1016/j.csda.2021.107343

user-defined tag

• 7 Affordable and Clean Energy