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ALTAF A. BHAT,M. YOUNUS BHAT,H. MAQBOOL,D.K. JAIN The Korean Society for Computational and Applied M 2023 Journal of applied mathematics & informatics Vol.41 No.2
In this paper we have obtained various forms of (p, q)-analogue of Aleph-Function satisfying Truesdell's ascending and descending F<sub>p,q</sub>-equation. These structures have been employed to arrive at certain generating functions for (p, q)-analogue of Aleph-Function. Some specific instances of these outcomes as far as (p, q)-analogue of I-function, H-function and G-functions have likewise been obtained.
(p, q)-ANALOGUE OF THE NATURAL TRANSFORM WITH APPLICATIONS
Altaf A. Bhat,Faiza A. Sulaiman,Javid A. Ganie,M. Younus Bhat,D.K. Jain 경남대학교 기초과학연구소 2023 Nonlinear Functional Analysis and Applications Vol.28 No.4
The natural transform is represented by two $(p,q)$-analogues,and their comparative characteristics are established. To resolve some $(p,q)$-difference and functional equations, applications are carried out.
Generating Operators of I-transform of the Mellin Convolution Type
Altaf A. Bhat,Javid A. Ganie,M. Younus Bhat,Faiza B. Suleiman 한국전산응용수학회 2024 Journal of applied mathematics & informatics Vol.42 No.1
In this paper, the I-transform of the Mellin convolution type is presented. Based on the Mellin transform theory, a general integral transform of the Mellin convolution type is introduced. The generating operators for I-transform together with the corresponding operational relations are also presented.
RAFIA GULZAR,IRSA SAJJAD,M. YOUNUS BHAT,SHAKEEL UL REHMAN The Korean Society for Computational and Applied M 2023 Journal of applied mathematics & informatics Vol.41 No.2
This paper deals in the study of the estimation of the parameters of Erlang distribution based on rank set sampling and some of its modifications. Here we considered Maximum Likelihood (ML) and the Bayesian technique to estimate the shape and scale parameter of Erlang distribution based on RSS and its some modifications such as ERSS, MRSS, and MRSSu. The derivation for unknown parameters of Erlang distribution is well presented using normal approximation to the asymptotic distribution of ML estimators. But due to the complexity involves in the integral, the Bayes estimator of unknown parameters is obtained using MCMC method. Further, we compared the MSE of estimation in different sampling schemes with different set sizes and cycle size. A real-life data application is also given to illustrate the efficiency of the proposed scheme.