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Estimation in the partially nonlinear model by continuous optimization

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  • Fatma Yerlikaya-Özkurt
  • Pakize Taylan
  • Müjgan Tez

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.

Suggested Citation

  • Fatma Yerlikaya-Özkurt & Pakize Taylan & Müjgan Tez, 2021. "Estimation in the partially nonlinear model by continuous optimization," Journal of Applied Statistics, Taylor & Francis Journals, vol. 48(13-15), pages 2826-2846, November.
    • Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2826-2846
    DOI: 10.1080/02664763.2020.1864816
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