Yazar "Weber, Gerhard Wilhelm" seçeneğine göre listele

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    Öğe
    C-LASSO estimator for generalized additive logistic regression based on B-Spline
    (Springer International Publishing, 2019) Taylan, Pakize; Weber, Gerhard Wilhelm; 0000-0003-0849-7771; 0000-0001-7204-8861
    [No abstract available]
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    Öğe
    CG-lasso estimator for multivariate adaptive regression spline
    (Springer International Publishing Ag, 2019) Taylan, Pakize; Weber, Gerhard Wilhelm
    [Abstract Not Available]
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    Öğe
    MATHEMATICAL AND DATA MINING CONTRIBUTIONS TO DYNAMICS AND OPTIMIZATION OF GENE-ENVIRONMENT NETWORKS
    (Nova Science Publishers, Inc, 2010) Weber, Gerhard Wilhelm; Taylan, Pakize; Akteke-Ozturk, Basak; Ugur, Omur
    This paper further introduces continuous optimization into the fields of computational biology and environmental protection which belong to the most challenging and emerging areas of science. It refines earlier ones of our models on gene-environment patterns by the use of optimization theory. We emphasize that it bases on and presents work done in [61, 66]. Furthermore, our paper tries to detect and overcome some structural frontiers of our methods applied to the recently introduced gene-environment networks. Based on the experimental data, we investigate the ordinary differential equations having nonlinearities on the right-hand side and a generalized treatment of the absolute shift term which represents the environmental effects. The genetic process is studied by a time-discretization, in particular, Runge-Kutta type discretization. The possibility of detecting stability and instability regions is being shown by a utilization of the combinatorial algorithm of Brayton and Tong which is based on the orbits of polyhedra. The time-continuous and discrete systems can be represented by means of matrices allowing biological implications, they encode and are motivated by our gene-environment networks. A specific contribution of this paper consists in a careful but rigorous integration of the environment into modeling and dynamics, and in further new sights. Relations to parameter estimation within modeling, especially, by using optimization, are indicated, and future research is addressed, especially towards the use of stochastic differential equations. This practically motivated and theoretically elaborated work is devoted for a contribution to better health care, progress in medicine, a better education and more healthy living conditions recommended.
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    Öğe
    Parameter estimation in stochastic differential equations
    (Springer Verlag, 2011) Weber, Gerhard Wilhelm; Taylan, Pakize; Görgülü, Zafer Korcan; Rahman, Haliza Abdul; Bahar, Arifah; 0000-0003-0849-7771; 0000-0002-1664-3452; 0000-0001-7204-8861
    Financial processes as processes in nature, are subject to stochastic fluctuations. Stochastic differential equations turn out to be an advantageous representation of such noisy, real-world problems, and together with their identification, they play an important role in the sectors of finance, but also in physics and biotechnology. These equations, however, are often hard to represent and to resolve. Thus we express them in a simplified manner of approximation by discretization and additive models based on splines. This defines a trilevel problem consisting of an optimization and a representation problem (portfolio optimization), and a parameter estimation (Weber et al. Financial Regression and Organization. In: Special Issue on Optimization in Finance, DCDIS-B, 2010). Two types of parameters dependency, linear and nonlinear, are considered by constructing a penalized residual sum of squares and investigating the related Tikhonov regularization problem for the first one. In the nonlinear case Gauss-Newton's method and Levenberg-Marquardt's method are employed in determining the iteration steps. Both cases are treated using continuous optimization techniques by the elegant framework of conic quadratic programming. These convex problems are well-structured, hence, allowing the use of the efficient interior point methods. Furthermore, we present nonparametric and related methods, and introduce into research done at the moment in our research groups which ends with a conclusion.