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Research Article | Open Access

A new very simply explicitly invertible approximation for the standard normal cumulative distribution function

Jessica Lipoth1Yoseph Tereda2Simon Michael Papalexiou3Raymond J. Spiteri2( )
Department of Electrical and Computer Engineering, College of Engineering, University of Saskatchewan, 57 Campus Dr, Canada
Department of Computer Science, College of Arts and Science, University of Saskatchewan, 176 Thorvaldson Building, 110 Science Place, Canada
Department of Civil Engineering, University of Calgary, Canada
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Abstract

This paper proposes a new very simply explicitly invertible function to approximate the standard normal cumulative distribution function (CDF). The new function was fit to the standard normal CDF using both MATLAB's Global Optimization Toolbox and the BARON software package. The results of three separate fits are presented in this paper. Each fit was performed across the range 0 z 7 and achieved a maximum absolute error (MAE) superior to the best MAE reported for previously published very simply explicitly invertible approximations of the standard normal CDF. The best MAE reported from this study is 2.73e–05, which is nearly a factor of five better than the best MAE reported for other published very simply explicitly invertible approximations.

CLC number: 65D10, 62J02

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AIMS Mathematics
Pages 11635-11646

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Cite this article:
Lipoth J, Tereda Y, Papalexiou SM, et al. A new very simply explicitly invertible approximation for the standard normal cumulative distribution function. AIMS Mathematics, 2022, 7(7): 11635-11646. https://doi.org/10.3934/math.2022648

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Received: 11 June 2021
Revised: 23 December 2021
Accepted: 10 April 2022
Published: 15 July 2022
©2022 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)