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Pliable regression spline estimator using auxiliary variables
Communications for Statistical Applications and Methods 2021;28:537-551
Published online September 30, 2021
© 2021 Korean Statistical Society.

Jae-Kwon Oha, Jae-Hwan Jhong1,a

aDepartment of Information Statistics, Chungbuk National University, Korea
Correspondence to: 1 Department of Information Statistics, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Korea. E-mail: jjh25@cbnu.ac.kr
This research was supported by Chungbuk National University Korea National University Development Project (2020).
Received April 15, 2021; Revised June 29, 2021; Accepted August 5, 2021.
 Abstract
We conducted a study on a regression spline estimator with a few pre-specified auxiliary variables. For the implementation of the proposed estimators, we adapted a coordinate descent algorithm. This was implemented by considering a structure of the sum of the residuals squared objective function determined by the B-spline and the auxiliary coefficients. We also considered an efficient stepwise knot selection algorithm based on the Bayesian information criterion. This was to adaptively select smoothly functioning estimator data. Numerical studies using both simulated and real data sets were conducted to illustrate the proposed method’s performance. An R software package psav is available.
Keywords : auxiliary variable, B-spline, coordinate descent algorithm, knot selection, nonparametric regression