On the foundations of parameter estimation for generalized partial linear models with B-splines and continuous optimization

Date

2010-07-01

Author

TAYLAN, PAKİZE
Weber, Gerhard Wilhelm
Liu, Lian
Yerlikaya-Ozkurt, Fatma

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Generalized linear models are widely used in statistical techniques. As an extension, generalized partial linear models utilize semiparametric methods and augment the usual parametric terms with a single nonparametric component of a continuous covariate. In this paper, after a short introduction, we present our model in the generalized additive context with a focus on the penalized maximum likelihood and the penalized iteratively reweighted least squares (P-IRLS) problem based on B-splines, which is attractive for nonparametric components. Then, we approach solving the P-IRLS problem using continuous optimization techniques. They have come to constitute an important complementary approach, alternative to the penalty methods, with flexibility for choosing the penalty parameter adaptively. In particular, we model and treat the constrained P-IRIS problem by using the elegant framework of conic quadratic programming. The method is illustrated using a small numerical example.

Subject Keywords

Modelling and Simulation, Computational Theory and Mathematics, Computational Mathematics

URI

https://hdl.handle.net/11511/56919

Journal

COMPUTERS & MATHEMATICS WITH APPLICATIONS

DOI

https://doi.org/10.1016/j.camwa.2010.04.040

Collections

Graduate School of Applied Mathematics, Article

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Citation Formats

P. TAYLAN, G. W. Weber, L. Liu, and F. Yerlikaya-Ozkurt, “On the foundations of parameter estimation for generalized partial linear models with B-splines and continuous optimization,” COMPUTERS & MATHEMATICS WITH APPLICATIONS, pp. 134–143, 2010, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/56919.