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Wu, C. and Yu, Y. (2014) Partially Linear Modeling of Conditional Quantiles Using Penalized Splines. Computational Statistics & Data Analysis, 77, 170-187.
https://doi.org/10.1016/j.csda.2014.02.020

has been cited by the following article:

• TITLE: Penalized Spline Estimation for Nonparametric Multiplicative Regression Models

  AUTHORS: Wei Chen, Mingzhen Wan, Jiahui Xu, Jing Zhong, Yuying Xia, Minmin Zhang

  KEYWORDS: Multiplicative Regression, Nonparametric Estimation, Penalized Splines, Relative Error, Smoothing Parameter Selection

  JOURNAL NAME: Open Access Library Journal, Vol.11 No.4, April 19, 2024

  ABSTRACT: In this paper, we consider the estimation problem of the unknown link function in the nonparametric multiplicative regression model. Combining the penalized splines technique, the least product relative error estimation method is proposed, where an effective model degree of freedom is de-fined, then the smoothing parameter is chosen by some information criteria. Simulation studies show that these strategies work well. Some asymptotic properties are established. A real data set is analyzed to illustrate the usefulness of the proposed approach. Finally, some possible extensions are discussed.