Abstract: This research proposes a systematic neuro-transfer function (neuro-TF) parametric modeling with a compact embedded format. Introducing transfer functions significantly enhances the ...
In this paper a new approach for obtaining an approximation global optimum solution of zero-one nonlinear programming (0-1 NP) problem which we call it Parametric Linearization Approach (P.L.A) is ...
On differentiability of implicitly defined function in semi-parametric profile likelihood estimation
This is a preview. Log in through your library . Abstract In this paper, we study the differentiability of implicitly defined functions which we encounter in the profile likelihood estimation of ...
ABSTRACT: We consider a Hilbert boundary value problem with an unknown parametric function on arbitrary infinite straight line passing through the origin. We propose to transform the Hilbert boundary ...
A robust non-parametric function fitting method is introduced. The estimate is motivated from the theory of M-estimation and of kernel estimation of regression functions. Consistency and asymptotic ...
Engage in lab-based activities designed to strengthen their problem-solving skills and expand knowledge of the topics in secondary mathematics, focusing especially on topics from precalculus and the ...
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