Numerical analysis: mathematics of scientific computing
Numerical analysis: mathematics of scientific computing
Iterative solution of nonlinear equations in several variables
Iterative solution of nonlinear equations in several variables
A mathematical view of interior-point methods in convex optimization
A mathematical view of interior-point methods in convex optimization
Estimation and testing in generalized partial linear models—A comparative study
Statistics and Computing
Generalized Additive Models (Texts in Statistical Science)
Generalized Additive Models (Texts in Statistical Science)
The new robust conic GPLM method with an application to finance: prediction of credit default
Journal of Global Optimization
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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-IRLS problem by using the elegant framework of conic quadratic programming. The method is illustrated using a small numerical example.