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Research Article | Open Access

A new generalized differential transform method for fractional ODEs with statistical applications

Ammar Abuualshaikh1Mahmoud Z. Aldrabseh2( )Tariq S. Alshammari3Khudhayr A. Rashedi3Khalid M. K. Alshammari3
School of Mathematical Science, Universiti Sains Malaysia, Penang, 11800 USM, Malaysia
Department of Mathematics, Faculty of Science, Zarqa University, Zarqa 13110, Jordan
Department of Mathematics, College of Science, University of Ha'il, Hail 55425, Saudi Arabia
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Abstract

This paper presents a new generalized differential transform method (NGDTM) in the solution of fractional-order differential equations. The technique is based on the generalized Taylor formula and the Riemann–Liouville fractional derivative. Theorems of fundamental transformation are developed based on rigorous proofs, and the convergence and uniqueness of the solutions obtained are proven. A number of linear and nonlinear examples such as models of statistical relevance are provided to demonstrate the accuracy and efficiency of the proposed approach. Besides, the classical exponential distribution is derived using the proposed NGDTM, and a new fractional exponential distribution is proposed on the same basis with the use of the same framework. The findings reveal that the technique provides very good approximate solutions and, in few instances, the exact solution by only few iterations, thus validating it as a tool of fractional differential equations as well as use in statistics.

CLC number: 60E05, 34A30

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AIMS Mathematics
Pages 9541-9562

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Cite this article:
Abuualshaikh A, Aldrabseh MZ, Alshammari TS, et al. A new generalized differential transform method for fractional ODEs with statistical applications. AIMS Mathematics, 2026, 11(4): 9541-9562. https://doi.org/10.3934/math.2026395

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Received: 25 January 2026
Revised: 26 March 2026
Accepted: 30 March 2026
Published: 10 April 2026
©2026 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)