Estimation in the partially nonlinear model by continuous optimization

Fatma Yerlikaya-Özkurt, Pakize Taylan and Müjgan Tez

Journal of Applied Statistics, 2021, vol. 48, issue 13-15, 2826-2846

Abstract: A 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.

Date: 2021
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DOI: 10.1080/02664763.2020.1864816

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