In this article, we make mathematical and practical contributions to the Bell-X family of absolutely continuous distributions. As a main member of this family, a special distribution extending the modeling perspectives of the famous Burr XII (BXII) distribution is discussed in detail. It is called the Bell-Burr XII (BBXII) distribution. It stands apart from the other extended BXII distributions because of its flexibility in terms of functional shapes. On the theoretical side, a linear representation of the probability density function and the ordinary and incomplete moments are among the key properties studied in depth. Some commonly used entropy measures, namely Rényi, Havrda and Charvat, Arimoto, and Tsallis entropy, are derived. On the practical (inferential) side, the associated parameters are estimated using seven different frequentist estimation methods, namely the methods of maximum likelihood estimation, percentile estimation, least squares estimation, weighted least squares estimation, Cramér von-Mises estimation, Anderson-Darling estimation, and right-tail Anderson-Darling estimation. A simulation study utilizing all these methods is offered to highlight their effectiveness. Subsequently, the BBXII model is successfully used in comparisons with other comparable models to analyze data on patients with acute bone cancer and arthritis pain. A group acceptance sampling plan for truncated life tests is also proposed when an item's lifetime follows a BBXII distribution. Convincing results are obtained.
Citation: Ayed. R. A. Alanzi, Muhammad Imran, M. H. Tahir, Christophe Chesneau, Farrukh Jamal, Saima Shakoor, Waqas Sami. Simulation analysis, properties and applications on a new Burr XII model based on the Bell-X functionalities[J]. AIMS Mathematics, 2023, 8(3): 6970-7004. doi: 10.3934/math.2023352
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Abstract
In this article, we make mathematical and practical contributions to the Bell-X family of absolutely continuous distributions. As a main member of this family, a special distribution extending the modeling perspectives of the famous Burr XII (BXII) distribution is discussed in detail. It is called the Bell-Burr XII (BBXII) distribution. It stands apart from the other extended BXII distributions because of its flexibility in terms of functional shapes. On the theoretical side, a linear representation of the probability density function and the ordinary and incomplete moments are among the key properties studied in depth. Some commonly used entropy measures, namely Rényi, Havrda and Charvat, Arimoto, and Tsallis entropy, are derived. On the practical (inferential) side, the associated parameters are estimated using seven different frequentist estimation methods, namely the methods of maximum likelihood estimation, percentile estimation, least squares estimation, weighted least squares estimation, Cramér von-Mises estimation, Anderson-Darling estimation, and right-tail Anderson-Darling estimation. A simulation study utilizing all these methods is offered to highlight their effectiveness. Subsequently, the BBXII model is successfully used in comparisons with other comparable models to analyze data on patients with acute bone cancer and arthritis pain. A group acceptance sampling plan for truncated life tests is also proposed when an item's lifetime follows a BBXII distribution. Convincing results are obtained.
1.
Introduction
Due to its well-established applications in various scientific and technical fields, fractional calculus has gained prominence during the last three decades. Many pioneers have shown that when adjusted by integer-order models, fractional-order models may accurately represent complex events [1,2]. The Caputo fractional derivatives are nonlocal in contrast to the integer-order derivatives, which are local in nature [1]. In other words, the integer-order derivative may be used to analyze changes in the area around a point, but the Caputo fractional derivative can be used to analyze changes in the whole interval. Senior mathematicians including Riemann [4], Caputo [5], Podlubny [6], Ross [7], Liouville [8], Miller and others, collaborated to create the fundamental foundation for fractional order integrals and derivatives. The theory of fractional-order calculus has been related to real-world projects, and it has been applied to chaos theory [9], signal processing [10], electrodynamics [11], human diseases [12,13], and other areas [14,15,16].
Due to the numerous applications of fractional differential equations in engineering and science such as electrodynamics [17], chaos ideas [18], accounting [19], continuum and fluid mechanics [20], digital signal [21] and biological population designs [22] fractional differential equations are now more widely known. For such issues to be resolved, efficient tools are needed [23,24,25]. Because of this, we will attempt to apply an efficient analytical technique to solve nonlinear arbitrary order differential equations in this article. Many strategies in collaboration fields may be delightfully and even more accurately analyzed using fractional differential equations. Various strategies have been developed in this regard, some of them are as follows, such as the fractional Reduced differential transformation technique [26], Adomian decomposition technique [27], the fractional Variational iteration technique [28], Elzaki decomposition technique [29,30], iterative transformation technique [31], the fractional natural decomposition method (FNDM) [32], and the fractional homotopy perturbation method [33].
The power series solution is used to solve some classes of the differential and integral equations of fractional or non-fractional order, and it is based on assuming that the solution of the equation can be expanded as a power series. RPS is an easy and fast technique for determining the coefficients of the power series solution. The Jordanian mathematician Omar Abu Arqub created the residual power series method in 2013, as a technique for quickly calculating the coefficients of the power series solutions for 1st and 2nd-order fuzzy differential equations [34]. Without perturbation, linearization, or discretization, the residual power series method provides a powerful and straightforward power series solution for highly linear and nonlinear equations [35,36,37,38]. The residual power series method has been used to solve an increasing variety of nonlinear ordinary and partial differential equations of various sorts, orders, and classes during the past several years. It has been used to make non-linear fractional dispersive partial differential equation have solitary pattern results and to predict them [39], to solve the highly nonlinear singular differential equation known as the generalized Lane-Emden equation [40], to solve higher-order ordinary differential equations numerically [41], to approximate solve the fractional nonlinear KdV-Burger equations, to predict and represent the RPSM differs from several other analytical and numerical approaches in some crucial ways [42]. First, there is no requirement for a recursion connection or for the RPSM to compare the coefficients of the related terms. Second, by reducing the associated residual error, the RPSM offers a straightforward method to guarantee the convergence of the series solution. Thirdly, the RPSM doesn't suffer from computational rounding mistakes and doesn't use a lot of time or memory. Fourth, the approach may be used immediately to the provided issue by selecting an acceptable starting guess approximation since the residual power series method does not need any converting when transitionary from low-order to higher-order and from simple linearity to complicated nonlinearity [43,44,45]. The process of solving linear differential equations using the LT method consists of three steps. The first step depends on transforming the original differential equation into a new space, called the Laplace space. In the second step, the new equation is solved algebraically in the Laplace space. In the last step, the solution in the second step is transformed back into the original space, resulting in the solution of the given problem.
In this article, we apply the Laplace residual power series method to achieve the definitive solution of the fractional-order nonlinear partial differential equations. The Laplace transformation efficiently integrates the residual power series method for the renewability algorithmic technique. This proposed technique produces interpretive findings in the sense of a convergent series. The Caputo fractional derivative operator explains quantitative categorizations of the partial differential equations. The offered methodology is well demonstrated in modelling and enumeration investigations. The exact-analytical findings are a valuable way to analyze the problematic dynamics of systems, notably for computational fractional partial differential equations.
2.
Preliminaries
Definition 2.1. The fractional Caputo derivative of a function u(ζ,t) of order α is given as [46]
CDαtu(ζ,t)=Jm−αtum(ζ,t),m−1<α≤m,t>0,
(2.1)
where m∈N and Jαt is the fractional integral Riemann-Liouville (RL) of u(ζ,t) of order α is given as
Jσtu(ζ,t)=1Γ(α)∫t0(t−τ)α−1u(φ,τ)dτ
(2.2)
Definition 2.2. The Laplace transformation (LT) of u(ζ,t) is given as [46]
Theorem 2.1.Let u(ζ,t) be a piecewise continuous function on I×[0,∞) with exponential order ζ. Assume that the fractional expansion of the function U(ζ,s)=Lt[u(ζ,t)] is as follows:
Now, we calculate fk(ζ,s), k=1,2,3,⋯, substituting the kth-truncate series of Eq (4.4) into the kth residual Laplace term Eq (4.6), multiply the solution equation by skα+1, and then solve recursively the link lims→∞(skα+1LtResu,k(ζ,s))=0, k=1,2,3,⋯. Following are the first some term:
f1(ζ,s)=24,f2(ζ,s)=−384,f3(ζ,s)=6144.
(4.7)
Putting the value of fk(ζ,s), k=1,2,3,⋯, in Eq (4.4), we get
In Figure 1, the exact and LRPSM solutions for u(ζ,t) at α=3 at ζ and t=0.3 of Example 4.1. In Figure 2, analytical solution for u(ζ,t) at different value of α=2.8 and 2.6 at ζ and t=0.3. In Figure 3, analytical solution for u(ζ,t) at various value of α at t=0.3 of Example 4.1.
Figure 1.
The actual and LRPSM results for u(ζ,t) at α=3 at ζ and t=0.3.
Now, we calculate fk(ζ,s), k=1,2,3,⋯, substituting the kth-truncate series of Eq (4.15) into the kth residual Laplace term Eq (4.16), multiply the solution equation by skα+1, and then solve recursively the link lims→∞(skα+1LtResu,k(ζ,s))=0, k=1,2,3,⋯. Following are the first some term:
In Figure 4, the exact and LRPSM solutions for u(ζ,t) at α=3 at ζ and t=0.3 of Example 4.2. In Figure 5, LRPSM solutions for u(ζ,t) at α=2.5 and α=2.8 and t=0.3 of Example 4.2.
Figure 4.
Exact and LRPSM solutions for u(ζ,t) at α=3 at ζ and t=0.3.
Now, we calculate fk(ζ,s), k=1,2,3,⋯, substituting the kth-truncate series of Eq (4.25) into the kth residual Laplace term Eq (4.26), multiply the solution equation by skα+1, and then solve recursively the link lims→∞(skα+1LtResu,k(ζ,s))=0, k=1,2,3,⋯. Following are the first some term:
f1(ζ,s)=−cosζ,f2(ζ,s)=cosζ,f3(ζ,s)=−cosζ.
(4.27)
Putting the value of fk(x,s), k=1,2,3,⋯, in Eq (4.25), we get
In this article, the fractional partial differential equation has been solved analytically by employing the Laplace residual power series method in conjunction with the Caputo operator. To demonstrate the validity of the recommended method, we analyzed three distinct partial differential equation problems. The simulation results demonstrate that the outcomes of our method are in close accordance with the exact answer. The new method is highly straightforward, efficient, and suitable for getting numerical solutions to partial differential equations. The primary advantage of the proposed approach is the series form solution, which rapidly converges to the exact answer. We can therefore conclude that the suggested approach is quite methodical and efficient for a more thorough investigation of fractional-order mathematical models.
Conflict of interest
The authors declare no conflicts of interest.
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Ayed. R. A. Alanzi, Muhammad Imran, M. H. Tahir, Christophe Chesneau, Farrukh Jamal, Saima Shakoor, Waqas Sami. Simulation analysis, properties and applications on a new Burr XII model based on the Bell-X functionalities[J]. AIMS Mathematics, 2023, 8(3): 6970-7004. doi: 10.3934/math.2023352
Ayed. R. A. Alanzi, Muhammad Imran, M. H. Tahir, Christophe Chesneau, Farrukh Jamal, Saima Shakoor, Waqas Sami. Simulation analysis, properties and applications on a new Burr XII model based on the Bell-X functionalities[J]. AIMS Mathematics, 2023, 8(3): 6970-7004. doi: 10.3934/math.2023352