Özkurt, Fatma YerlikayaTaylan, PakizeTez, Müjgan2024-03-062024-03-062021Özkurt, F. Y., Taylan, P. ve Tez, M. (2021). Estimation in the partially nonlinear model by continuous optimization. Journal of Applied Statistics, 48(13-15), 2826-2846.0266-4763https://www.tandfonline.com/doi/full/10.1080/02664763.2020.1864816https://hdl.handle.net/11468/13518A 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/openAccessB-splineContinuous optimizationEstimationNonlinear modelNonparametric regressionArticle4813-1528262846WOS:0006013444000012-s2.0-850980114473570706510.1080/02664763.2020.1864816Q1Q3