Yerlikaya-Ozkurt, FatmaTaylan, PakizeTez, MujganIndustrial Engineering2024-07-052024-07-05202110266-47631360-053210.1080/02664763.2020.18648162-s2.0-85098011447https://doi.org/10.1080/02664763.2020.1864816https://hdl.handle.net/20.500.14411/1954A useful model for data analysis is the partially nonlinear model where response variable is represented as the sum of a nonparametric and a parametric component. In this study, we propose a new procedure for estimating the parameters in the partially nonlinear models. Therefore, we consider penalized profile nonlinear least square problem where nonparametric components are expressed as a B-spline basis function, and then estimation problem is expressed in terms of conic quadratic programming which is a continuous optimization problem and solved interior point method. An application study is conducted to evaluate the performance of the proposed method by considering some well-known performance measures. The results are compared against parametric nonlinear model.eninfo:eu-repo/semantics/openAccessNonlinear modelnonparametric regressionestimationB-splinecontinuous optimizationArticleQ24813-1528262846WOS:00060134440000135707065