Fractional Mean-Square Inequalities for (P, m)-Superquadratic Stochastic Processes and Their Applications to Stochastic Divergence Measures

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Pubblicato in:Fractal and Fractional vol. 9, no. 12 (2025), p. 771-804
Autore principale: Khan, Dawood
Altri autori: Butt, Saad Ihsan, Jallani Ghulam, Alammar Mohammed, Seol Youngsoo
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MDPI AG
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022 |a 2504-3110 
024 7 |a 10.3390/fractalfract9120771  |2 doi 
035 |a 3286294584 
045 2 |b d20250101  |b d20251231 
100 1 |a Khan, Dawood  |u Department of Mathematics, University of Balochistan, Quetta 87300, Pakistan; dawooddawood601@gmail.com 
245 1 |a Fractional Mean-Square Inequalities for (P, m)-Superquadratic Stochastic Processes and Their Applications to Stochastic Divergence Measures 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a In this study, we introduce and rigorously formalize the notion of (P, m)-superquadratic stochastic processes, representing a novel and far-reaching generalization of classical convex stochastic processes. By exploring their intrinsic structural characteristics, we establish advanced Jensen and Hermite–Hadamard (<inline-formula>H.H</inline-formula>)-type inequalities within the mean-square stochastic calculus framework. Furthermore, we extend these inequalities to their fractional counterparts via stochastic Riemann–Liouville (<inline-formula>RL</inline-formula>) fractional integrals, thereby enriching the analytical machinery available for fractional stochastic analysis. The theoretical findings are comprehensively validated through graphical visualizations and detailed tabular illustrations, constructed from diverse numerical examples to highlight the behavior and accuracy of the proposed results. Beyond their theoretical depth, the developed framework is applied to information theory, where we introduce new classes of stochastic divergence measures. The proposed results significantly refine the approximation of stochastic and fractional stochastic differential equations governed by convex stochastic processes, thereby enhancing the precision, stability, and applicability of existing stochastic models. To ensure reproducibility and computational transparency, all graph-generation commands, numerical procedures, and execution times are provided, offering a complete and verifiable reference for future research in stochastic and fractional inequality theory. 
653 |a Cryptography 
653 |a Control theory 
653 |a Calculus 
653 |a Divergence 
653 |a Stochastic processes 
653 |a Inequalities 
653 |a Mathematical analysis 
653 |a Computer science 
653 |a Random variables 
653 |a Mathematical models 
653 |a Integrals 
653 |a Design 
653 |a Convex analysis 
653 |a Stochastic models 
653 |a Engineering 
653 |a Mathematics 
653 |a Fractional calculus 
653 |a Information theory 
653 |a Differential equations 
653 |a Data compression 
700 1 |a Butt, Saad Ihsan  |u Department of Mathematics, COMSATS University Islamabad, Lahore Campus, Lahore 54000, Pakistan; saadihsanbutt@gmail.com (S.I.B.); fa23-pmt-002@cuilahore.edu.pk (G.J.) 
700 1 |a Jallani Ghulam  |u Department of Mathematics, COMSATS University Islamabad, Lahore Campus, Lahore 54000, Pakistan; saadihsanbutt@gmail.com (S.I.B.); fa23-pmt-002@cuilahore.edu.pk (G.J.) 
700 1 |a Alammar Mohammed  |u Applied College, Shaqra University, Shaqra 11961, Saudi Arabia; alammar@su.edu.sa 
700 1 |a Seol Youngsoo  |u Department of Mathematics, Dong-A University, Busan 49315, Republic of Korea 
773 0 |t Fractal and Fractional  |g vol. 9, no. 12 (2025), p. 771-804 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3286294584/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch 
856 4 0 |3 Full Text + Graphics  |u https://www.proquest.com/docview/3286294584/fulltextwithgraphics/embedded/75I98GEZK8WCJMPQ?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3286294584/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch