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


Taylan P., Weber G. -. , Beck A.

OPTIMIZATION, vol.56, pp.675-698, 2007 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 56
  • Publication Date: 2007
  • Doi Number: 10.1080/02331930701618740
  • Journal Name: OPTIMIZATION
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.675-698
  • Keywords: regression, generalized additive model, statistical learning, clustering separation of variables, density, variation, curvature, backfitting (Gauss-Seidel) algorithm, penalty methods, classification, continous optimization, conic quadratic programming, financial mathematics

Abstract

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..