New approaches to regression by generalized additive models and continuous optimization for modern applications in finance, science and technology

Date

2007-10-01

Author

Taylan, P.
Weber, Gerhard Wilhelm
Beck, A.

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Generalized additive models belong to modern techniques frorn statistical learning, and are applicable in many areas of prediction, e.g. in financial mathamatics, computational biology, medicine, chemistry and environmental protection. In these models, the expectation of response is linked to the predictors via a link function. These models are fitted through local scoring algorithm using it scatterplot smoother as building blocks proposed by Hastie and Tibshirani (1987). In this article, we first give it short introduction and review. Then, we present a mathematical modeling by splines based on a new clustering approach for the x, their density, and the variation of output y. We contribute to regression with generalized additive models by bounding (penalizing) second-order terms (curvature) of the splines, leading to a more robust approximation. Previously, in [23], we proposed it refining modification and investigation of the backfitting algorithm, applied to additive models. Then, because of drawbacks of the modified backfitting algorithm, we solve this problem using continuous optimization techniques, which will becorne an important complementary technology and alternative to the concept of modified backfitting algorithm. In particular, we model and treat the constrained residual sum of squares by the elegant Framework of conic quadratic programming..

Subject Keywords

Management Science and Operations Research, Control and Optimization, Applied Mathematics

URI

https://hdl.handle.net/11511/52483

Journal

OPTIMIZATION

DOI

https://doi.org/10.1080/02331930701618740

Collections

Graduate School of Applied Mathematics, Article

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Citation Formats

P. Taylan, G. W. Weber, and A. Beck, “New approaches to regression by generalized additive models and continuous optimization for modern applications in finance, science and technology,” OPTIMIZATION, pp. 675–698, 2007, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/52483.