An Iterative Algorithm Using the Statistical Perspective of Bias for Efficient Polynomial Approximation by Modified MKZ Operator
by Robin Antoine andAshok Sahai.
This paper aims at constructing an iterative computerizable numerical algorithm for an improved polynomial approximation by a modified version of ‘MKZ’ operator. The algorithm uses the ‘Statistical Perspective of Bias’ for exploiting the information about the unknown function ‘f’ available in terms of its known values at the ‘pre-chosen-knots’ in C [0, 1/2] more fully with the proposed modified operator. The improvement, achieved by an a-posteriori use of this information, happens iteratively. Any typical iteration uses the typical concepts of ‘Bias’. The potential of the achievable efficiency through the proposed ‘computerizable numerical iterative algorithm’ is illustrated per an ‘empirical study’ for which the function ‘f’ is assumed to be known in the sense of simulation. The illustration has been confined to “Three Iterations” only, for the sake of simplicity of illustration.
Approximation; simulated empirical study
Robin M. Antoine, email@example.com
Ashok Sahai, firstname.lastname@example.org
Ahmed H. Youssef,email@example.com
READING THE ARTICLE: You can read the article in
portable document (.pdf) format (268385 bytes.)
NOTE: The content of this article is the intellectual property of the authors, who retains all rights to future publication.
This page has been accessed 2528 times since JUNE 7, 2010.
Return to the Home Page.