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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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BibTeX
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.