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Research article

A new extension of the Rayleigh distribution: Methodology, classical, and Bayes estimation, with application to industrial data

  • Received: 09 December 2024 Revised: 07 February 2025 Accepted: 12 February 2025 Published: 25 February 2025
  • MSC : 60B12, 62G30

  • In statistical modeling, generating a novel family of distributions is essential to develop new and adaptable models to analyze various data sets. This paper presents a new asymmetric extension of the Rayleigh distribution called the generalized Kumaraswamy Rayleigh model. The proposed distribution can fit symmetric, complex, heavy-tailed, and asymmetric data sets. Several key mathematical and statistical results were investigated, including moments, moment-generating functions, variance, dispersion index, skewness, and kurtosis for the suggested model. In addition, various estimation strategies, including maximum likelihood estimation and Bayes estimation, were used to estimate the model parameters. The Metropolis-Hastings technique was used for Bayesian estimates under the square error loss function. A comprehensive simulation study was used to evaluate the performance of the derived estimators. The model's flexibility was tested on two data sets from the industrial domain, revealing that it offers greater flexibility compared to existing distributions.

    Citation: Alanazi Talal Abdulrahman, Khudhayr A. Rashedi, Tariq S. Alshammari, Eslam Hussam, Amirah Saeed Alharthi, Ramlah H Albayyat. A new extension of the Rayleigh distribution: Methodology, classical, and Bayes estimation, with application to industrial data[J]. AIMS Mathematics, 2025, 10(2): 3710-3733. doi: 10.3934/math.2025172

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  • In statistical modeling, generating a novel family of distributions is essential to develop new and adaptable models to analyze various data sets. This paper presents a new asymmetric extension of the Rayleigh distribution called the generalized Kumaraswamy Rayleigh model. The proposed distribution can fit symmetric, complex, heavy-tailed, and asymmetric data sets. Several key mathematical and statistical results were investigated, including moments, moment-generating functions, variance, dispersion index, skewness, and kurtosis for the suggested model. In addition, various estimation strategies, including maximum likelihood estimation and Bayes estimation, were used to estimate the model parameters. The Metropolis-Hastings technique was used for Bayesian estimates under the square error loss function. A comprehensive simulation study was used to evaluate the performance of the derived estimators. The model's flexibility was tested on two data sets from the industrial domain, revealing that it offers greater flexibility compared to existing distributions.



    Presently, the differential systems of non-integer orders have gained wide prominence due to their great relevance in describing several real-world problems in physics, mechanics and engineering. For instance, we refer the reader to the monographs of Baleanu et al.[12], Hilfer [28], Kilbas et al. [31], Mainardi [33], Miller and Ross [34], Podlubny [37], Samko et al. [39] and the papers [22,40].

    During the study of some phenomena, a number of researchers realized the importance of fractional operators with non-singular kernels which can model practical physical phenomena well like the heat transfer model, the diffusion equation, electromagnetic waves in dielectric media, and circuit model (see [7,9,23,24] and the references existing therein).

    Caputo and Fabrizio in [11] studied a new kind of fractional derivative with an exponential kernel. Atangana and Baleanu [10] introduced a new operator with fractional order based upon the generalized Mittag-Leffler function. Their newly fractional operator involves kernel being nonlocal and nonsingular. The nonlocality of the kernel gives better description of the memory within the structure with different scale. Abdeljawad [4] extended this fractional derivative from order between zero and one to higher arbitrary order and formulated their associated integral operators.

    On the other hand, the theory of measure of non-compactness is an essential tool in investigating the existence of solutions to nonlinear integral and differential equations, see, for example, the recent papers [6,14,20,26,38] and the references existing therein.

    In [19], Benchohra et al. studied the existence of solutions for the fractional differential inclusions with boundary conditions

    {CDry(t)G(t,y(t)),a.e. on[0,T],1<r<2,y(0)=y0,y(T)=yT, (1.1)

    where CDr is the Caputo fractional derivative, G:[0,T]×EP(E) is a multi-valued map, y0,yTE and (E,||) is a Banach space and P(E)={ZP(E):Z}.

    In the present work, we are interested in studying the existence of solutions for the following nonlinear fractional differential inclusions with ABC fractional derivatives

    {ABCaDαx(t)F(t,x(t)),a.e. onJ:=[a,b],x(a)=x(a)=0, (1.2)

    where ABCaDα denotes the ABC fractional derivative of order α(1,2], (E,||) is a Banach space, P(E) is the family of all nonempty subsets of E, and F:J×EP(E) is a given multi-valued map. We study the ABC fractional inclusion (1.2) in the case where the right hand side is convex-valued by means of the set-valued issue of Mönch fixed point theorem incorporated with the Kuratowski measure of non-compactness.

    Differential inclusions play an important role as a tool in the study of various dynamical processes described by equations with a discontinuous or multivalued right-hand side, occurring, in particular, in the study of dynamics of economical, social, and biological macrosystems. They also are very useful in proving existence theorems in control theory.

    Due to the importance of fractional differential inclusions in mathematical modeling of problems in game theory, stability, optimal control, and so on. For this reason, many contributions have been investigated by some researchers [1,2,3,5,8,16,17,18,25,30,35].

    It is worth noting that the results included with the fractional differential inclusions with the ABC fractional derivatives in Banach spaces are rather few, so the outputs of this paper are a new addition for the development of this topic.

    First at all, we recall the following definition of Riemann-Liouville fractional integral.

    Definition 2.1. [31] Take α>0,aR, and v a real-valued function defined on [a,). The Riemann-Liouville fractional integral is defined by

    aIαv(t)=1Γ(α)ta(ts)α1v(s)ds.

    Next, we present the basic definitions of the new fractional operator due to Atangana and Baleanu [10] and the extended ones due to Abdeljawad [4].

    Definition 2.2. [10] Take α[0,1] and vH1(a,b),a<b, then the ABC fractional derivative is given by

    ABCaDαv(t)=B(α)1αtaEα[α(ts)α1α]v(s)ds, (2.1)

    where B(α)=α2α>0 denotes the normalization function such that B(0)=B(1)=1 and Eα denotes the Mittag-Leffler function defined by

    Eα(t)=k=0tkΓ(αk+1).

    The associated Atangana-Baleanu (AB) fractional integral by

    ABaIαv(t)=1αB(α)v(t)+αB(α)Γ(α)ta(ts)α1v(s)ds. (2.2)

    The following definitions concern with the higher order case.

    Definition 2.3. [4] Take α(m,m+1], for some mN0, and v be such that v(n)H1(a,b). Set β=αm. Then β(0,1] and we define the ABC fractional derivative by

    ABCaDαv(t)=ABCaDβv(m)(t). (2.3)

    In the light of the convention v(0)(t)=v(t), one has ABCaDαv(t)=ABCaDαv(t) for α(0,1].

    The correspondent fractional integral is given by

    ABCaIαv(t)=aImABCaIαv(t). (2.4)

    Lemma 2.4. [4] For u(t) defined on [a,b] and α(m,m+1], for some mN0, we obtain that

    ABCaIαABCaDαu(t)=u(t)mk=0u(k)k!(ta)k.

    Denote by C(J,E) the Banach space of all continuous functions from J to E with the norm x=suptJ|x(t)|. By L1(J,E), we indicate the space of Bochner integrable functions from J to E with the norm x1=b0|x(t)|dt.

    Let the Banach space be (E,||). The expressions we have used are P(E)={ZP(E):Z}, Pcl(E)={ZP(E):Z isclosed}, Pbd(E)={ZP(E):Z isbounded}, Pcp(E)={ZP(E):Z iscompact}, Pcvx(E)={ZP(E):Z isconvex}.

    ● A multi-valued map U:EP(E) is convex (closed) valued, if U(x) is convex (closed) for all xE.

    U is bounded on bounded sets if U(B)=xBU(x) is bounded in E for any BPbd(E), i.e. supxB{sup{y:yU(x)}}<.

    U is called upper semi-continuous on E if for each xE, the set U(x) is nonempty, closed subset of E, and if for each open set N of E containing U(x), there exists an open neighborhood N of x such that U(N)N.

    U is completely continuous if U(B) is relatively compact for each BPbd(E).

    ● If U is a multi-valued map that is completely continuous with nonempty compact values, then U is u.s.c. if and only if U has a closed graph (that is, if xnx0,yny0, and ynU(xn), then y0U(x0).

    For more details about multi-valued maps, we refer to the books of Deimling [21] and Hu and Papageorgiou [29].

    Definition 2.5. A multi-valued map F:J×EP(E) is said to be Carathéodory if

    (i) tF(t,x) is measurable for each uE;

    (ii) xF(t,x) is upper semi-continuous for almost all tJ.

    We define the set of the selections of a multi-valued map F by

    SF,x:={fL1(J,E):f(t)F(t,x(t))fora.e.tJ}.

    Lemma 2.6. [32] Let J be a compact real interval and E be a Banach space. Let F be a multi-valued map satisfying the Carathèodory conditions with the set of L1-selections SF,u nonempty, and let Θ:L1(J,E)C(J,E) be a linear continuous mapping. Then the operator

    ΘSF,x:C(J,E)Pbd,cl,cvx(C(J,E)),x(ΘSF,x)(x):=Θ(SF,x)

    is a closed graph operator in C(J,E)×C(J,E).

    We specify this part of the paper to explore some important details of the Kuratowski measure of non-compactness.

    Definition 2.7. [15] Let ΛE be the family of bounded subsets of a Banach space E. We define the Kuratowski measure of non-compactness κ:ΛE[0,] of BΛE as

    κ(B)=inf{ϵ>0:Bmj=1Bj  and diam(Bj)ϵ},

    for some mN and BjE.

    Lemma 2.8. [15] Let C,DE be bounded, the Kuratowski measure of non-compactness possesses the next characteristics:

    i. κ(C)=0C is relatively compact;

    ii. CDκ(C)κ(D);

    iii. κ(C)=κ(¯C), where ¯C is the closure of C;

    iv. κ(C)=κ(conv(C)), where conv(C) is the convex hull of C;

    v. κ(C+D)κ(C)+κ(D), where C+D={u+v:uC,vD};

    vi. κ(νC)=|ν|κ(C), for any νR.

    Theorem 2.9. (Mönch's fixed point theorem)[36] Let Ω be a closed and convex subset of a Banach space E; U a relatively open subset of Ω, and N:¯UP(Ω). Assume that graph N is closed, N maps compact sets into relatively compact sets and for some x0U, the following two conditions are satisfied:

    (i)G¯U, Gconv(x0N(G)), ¯G=¯C implies ¯G is compact, where C is a countable subset of G;

    (ii) x(1μ)x0+μN(x)u¯UU,μ(0,1).

    Then there exists x¯U with xN(x).

    Theorem 2.10. [27] Let E be a Banach space and CL1(J,E) countable with |u(t)|h(t) for a.e. tJ, and every uC; where hL1(J,R+). Then the function z(t)=κ(C(t)) belongs to L1(J,R+) and satisfies

    κ({b0u(τ)dτ:uC})2b0κ(C(τ))dτ.

    We start this section with the definition of a solution of the ABC fractional inclusion (1.2).

    Definition 3.1. A function xC(J,E) is said to be a solution of the ABC fractional inclusion (1.2) if there exist a function fL1(J,E) with f(t)F(t,x(t)) for a.e. tJ, such that ABCaDαtx(t)=f(t) on J, and the conditions x(a)=x(a)=0 are satisfied.

    Lemma 3.2. [4] For any hC(J,R), the solution x of the linear ABC fractional differential equation

    {ABCaDαx(t)=h(t),tJ,x(a)=x(a)=0, (3.1)

    is given by the following integral equation

    x(t)=2αB(α1)tah(s)ds+α1B(α1)1Γ(α)ta(ts)α1h(s)ds,tJ. (3.2)

    Remark 3.3. The result of Lemma 3.2 is true not only for real valued functions xC(J,R) but also for a Banach space functions xC(J,E).

    Lemma 3.4. Assume that F:J×EP(E) satisfies Carathèodory conditions, i.e., tF(t,x) is measurable for every xE and xF(t,x) is continuous for every tJ. A function xC(J,E) is a solution of the ABC fractional inclusion (1.2) if and only if it satisfies the integral equation

    x(t)=2αB(α1)taf(s)ds+α1B(α1)1Γ(α)ta(ts)α1f(s)ds, (3.3)

    where fL1(J,E) with f(t)F(t,x(t)) for a.e. tJ.

    Now, we are ready to present the main result of the current paper.

    Theorem 3.5. Let ϱ>0,K={xE:xϱ},U={xC(J,E):x<ϱ}, and suppose that:

    (H1) The multi-valued map F:J×EPcp,cvx(E) is Carathèodory.

    (H2) For each ϱ>0, there exists a function φL1(J,R+) such that

    F(t,x)P={|f|:f(t)F(t,x)}φ(t),

    for a.e. tJ and xE with |x|ϱ, and

    limϱinfbaφ(t)dtϱ=<.

    (H3) There is a Carathèodory function ϑ:J×[0,2ϱ]R+ such that

    κ(F(t,G))ϑ(t,κ(G)),

    a.e. tJ and each GK, and the unique solution θC(J,[a,2ϱ]) of the inequality

    θ(t)2{2αB(α1)taϑ(s,κ(G(s)))ds+α1B(α1)1Γ(α)ta(ts)α1ϑ(s,κ(G(s)))ds},tJ,

    is θ0.

    Then the ABC fractional inclusion (1.2) possesses at least one solution, provided that

    <[(2α)(ba)B(α1)+α1B(α1)(ba)αΓ(α+1)]1. (3.4)

    Proof. Define the multi-valued map N:C(J,E)P(C(J,E)) by

    (Nx)(t)={fC(J,E):f(t)=2αB(α1)taw(s)ds+α1B(α1)1Γ(α)ta(ts)α1w(s)ds,wSF,x. (3.5)

    In accordance with Lemma 3.4, the fixed points of N are solutions to the ABC fractional inclusion (1.2). We shall show in five steps that the multi-valued operator N satisfies all assumptions of Mönch's fixed point theorem (Theorem 2.9) with ¯U=C(J,K).

    Step 1. N(x) is convex, for any xC(J,K).

    For f1,f2N(x), there exist w1,w2SF,x such that for each tJ, we have

    fi(t)=2αB(α1)tawi(s)ds+α1B(α1)1Γ(α)ta(ts)α1wi(s)ds,i=1,2.

    Let 0μ1. Then for each tJ, one has

    (μf1+(1μ)f2)(t)=2αB(α1)ta(μw1(s)+(1μ)w2(s))ds+α1B(α1)1Γ(α)ta(ts)α1(μw1(s)+(1μ)w2(s))ds.

    Since SF,x is convex (forasmuch F has convex values), then μf1+(1μ)f2N(x).

    Step 2. N(G) is relatively compact for each compact G¯U.

    Let G¯U be a compact set and let {fn} be any sequence of elements of N(G). We show that {fn} has a convergent subsequence by using the Arzelà-Ascoli criterion of non-compactness in C(J,K). Since fnN(G), there exist xnG and wnSF,xn, such that

    fn(t)=2αB(α1)tawn(s)ds+α1B(α1)1Γ(α)ta(ts)α1wn(s)ds,

    for n1. In view of Theorem 2.10 and the properties of the Kuratowski measure of non-compactness, we have

    κ({fn(t)})2{2αB(α1)taκ({wn(s):n1})ds+α1B(α1)1Γ(α)taκ({(ts)α1wn(s):n1})ds}. (3.6)

    On the other hand, since G is compact, the set {wn(τ):n1} is compact. Consequently, κ({wn(s):n1})=0 for a.e. sJ. Likewise,

    κ({(ts)α1wn(s):n1})=(ts)α1κ({wn(s):n1})=0,

    for a.e. t,sJ. Therefore, (3.6) implies that {fn(t):n1} is relatively compact in K for each tJ.

    Furthermore, For each t1,t2J,t1<t2, one obtain that:

    |fn(t2)fn(t1)|=|2αB(α1)t2t1wn(s)ds+α1B(α1)1Γ(α)t1a[(t2s)α1(t1s)α1]wn(s)ds+α1B(α1)1Γ(α)t2t1(t2s)α1wn(s)ds||2αB(α1)t2t1wn(s)ds|+|α1B(α1)1Γ(α)t1a[(t2s)α1(t1s)α1]wn(s)ds|+|α1B(α1)1Γ(α)t2t1(t2s)α1wn(s)ds|2αB(α1)t2t1φ(s)ds+α1B(α1)1Γ(α)t1a|(t2s)α1(t1s)α1|φ(s)ds+α1B(α1)1Γ(α)t2t1|(t2s)α1|φ(s)ds

    As t1t2, the right hand side of the above inequality tends to zero. Thus, {wn(τ):n1} is equicontinuous. Hence, {wn(τ):n1} is relatively compact in C(J,K).

    Step 3. The graph of N is closed.

    Let xnx,fnN(xn), and fnf. It must be to show that fN(x). Now, fnN(xn) means that there exists wnSF,xn such that, for each tJ,

    fn(t)=2αB(α1)tawn(s)ds+α1B(α1)1Γ(α)ta(ts)α1wn(s)ds.

    Consider the continuous linear operator Θ:L1(J,E)C(J,E),

    Θ(w)(t)fn(t)=2αB(α1)tawn(s)ds+α1B(α1)1Γ(α)ta(ts)α1wn(s)ds.

    It is obvious that fnf0 as n. Therefore, in the light of Lemma 2.6, we infer that ΘSF is a closed graph operator. Additionally, fn(t)Θ(SF,xn). Since, xnx, Lemma 2.6 gives

    f(t)=2αB(α1)taw(s)ds+α1B(α1)1Γ(α)ta(ts)α1w(s)ds,

    for some wSF,x.

    Step 4. G is relatively compact in C(J,K).

    Assume that G¯U,Gconv({0}N(G)), and ¯G=¯C for some countable set CG. Using a similar approach as in Step 2, one can obtain that N(G) is equicontinuous. In accordance to Gconv({0}N(G)), it follows that G is equicontinuous. In addition, since CGconv({0}N(G)) and C is countable, then we can find a countable set P={fn:n1}N(G) with Cconv({0}P). Thus, there exist xnG and wnSF,xn such that

    fn(t)=2αB(α1)tawn(s)ds+α1B(α1)1Γ(α)ta(ts)α1wn(s)ds.

    In the light of Theorem 2.10 and the fact that G¯C¯conv({0}P), we get

    κ(G(t))κ(¯C(t))κ(P(t))=κ({fn(t):n1}).

    By virtue of (3.6) and the fact that wn(τ)G(τ), we get

    κ(G(t))2{2αB(α1)taκ({wn(s):n1})ds+α1B(α1)1Γ(α)taκ({(ts)α1wn(s):n1})ds}2{2αB(α1)taκ(G(s))ds+α1B(α1)1Γ(α)ta(ts)α1κ(G(s))ds}2{2αB(α1)taϑ(s,κ(G(s)))ds+α1B(α1)1Γ(α)ta(ts)α1ϑ(s,κ(G(s)))ds}.

    Also, the function θ given by θ(t)=κ(G(t)) belongs to C(J,[a,2ϱ]). Consequently by (H3), θ0, that is κ(G(t))=0 for all tJ.

    Now, by the Arzelà-Ascoli theorem, G is relatively compact in C(J,K).

    Step 5. Let fN(x) with x¯U. Since x(τ)ϱ and (H2), we have N(¯U)¯U, because if it is not true, there exists a function x¯U but N(x)>ϱ and

    f(t)=2αB(α1)taw(s)ds+α1B(α1)1Γ(α)ta(ts)α1w(s)ds,

    for some wSF,x. On the other hand, we have

    ϱ<N(x)2αB(α1)ta|w(s)|ds+α1B(α1)1Γ(α)ta(ts)α1|w(s)|ds2αB(α1)baφ(s)ds+α1B(α1)1Γ(α)ba(bs)α1φ(s)ds[(2α)(ba)B(α1)+α1B(α1)(ba)αΓ(α+1)]baφ(s)ds.

    Dividing both sides by ϱ and taking the lower limit as ϱ, we infer that

    [(2α)(ba)B(α1)+α1B(α1)(ba)αΓ(α+1)]1,

    which contradicts (3.4). Hence N(¯U)¯U.

    As a consequence of Steps 1–5 together with Theorem 2.9, we infer that N possesses a fixed point xC(J,K) which is a solution of the ABC fractional inclusion (1.2).

    Consider the fractional differential inclusion

    {ABC0D32x(t)F(t,x(t)),a.e. on[0,1],x(0)=x(1)=0, (4.1)

    where α=32,a=0,b=1, and F:[0,1]×RP(R) is a multi-valued map given by

    xF(t,x)=(e|x|+sint,3+|x|1+x2+5t3).

    For fF, one has

    |f|=max(e|x|+sint,3+|x|1+x2+5t3)9,xR.

    Thus

    F(t,x)P={|f|:fF(t,x)}=max(e|x|+sint,3+|x|1+x2+5t3)9=φ(t),

    for t[0,1],xR. Obviously, F is compact and convex valued, and it is upper semi-continuous.

    Furthermore, for (t,x)[0,1]×R with |x|ϱ, one has

    limϱinf10φ(t)dtϱ=0=.

    Therefore, the condition (3.4) implies that

    [(2α)(ba)B(α1)+α1B(α1)(ba)αΓ(α+1)]10.77217>0.

    Finally, we assume that there exists a Carathèodory function ϑ:[0,1]×[0,2ϱ]R+ such that

    κ(F(t,G))ϑ(t,κ(G)),

    a.e. t[0,1] and each GK={xR:|x|ϱ}, and the unique solution θC([0,1],[0,2ϱ]) of the inequality

    θ(t)2{1Γ(α1)t0u0exp(tuk1(α,s)k0(α,s)ds)(uτ)α2k0(α,u)ϑ(τ,κ(G(τ)))dτdu},tJ,

    is θ0.

    Hence all the assumptions of Theorem 3.5 hold true and we infer that the ABC fractional inclusion (4.1) possesses at least one solution on [0,1].

    In this paper, we extend the investigation of fractional differential inclusions to the case of the ABC fractional derivatives in Banach space. Based on the set-valued version of Mönch fixed point theorem together with the Kuratowski measure of non-compactness, the existence theorem of the solutions for the proposed ABC fractional inclusions is founded. An clarified example is suggested to understand the theoretical finding.

    The paper is partially supported by "PIAno di inCEntivi per la RIcerca di Ateneo 2020/2022". The second author is gratefully supported by "RUDN University Strategic Academic Leadership Program".

    The authors declare no conflict of interest.



    [1] V. Verevka, E. Epichenko, Developing a model for predicting bankruptcy of construction industry enterprises, Econom. Anal. Theory Pract., 23 (2024), 878–892. https://doi.org/10.24891/ea.23.5.878 doi: 10.24891/ea.23.5.878
    [2] V. V. Barskov, Y. A. Dubolazova, A. A. Maykova, E. A. Konnikov, Modeling the probability of companies bankruptcy in the construction industry, Soft Meas. Comput., 2 (2023), 5–15. https://doi.org/10.36871/2618-9976.2024.02.001 doi: 10.36871/2618-9976.2024.02.001
    [3] A. Alzaatreh, C. Lee, F. Famoye, A new method for generating families of continuous distributions, METRON, 71 (2013), 63–79. https://doi.org/10.1007/s40300-013-0007-y doi: 10.1007/s40300-013-0007-y
    [4] T. G. Ieren, S. S. Abdulkadir, A. A. Issa, Odd Lindley- Rayleigh distribution its properties and applications to simulated and real life datasets, J. Adv. Math. Comput. Sci., 35 (2020), 68–88. https://doi.org/10.9734/jamcs/2020/v35i130240 doi: 10.9734/jamcs/2020/v35i130240
    [5] F. H. Riad, B. Alruwaili, E. M. Almetwally, E. Hussam, Fuzzy reliability analysis of the COVID‐19 mortality rate using a new modified Kies Kumaraswamy model, J. Math., 2022 (2022), 3427521. https://doi.org/10.1155/2022/3427521 doi: 10.1155/2022/3427521
    [6] A. EL-Helbawy, M. Hegazy, A. Abd EL-Hady, Statistical properties and applications of the discrete exponentiated modified Topp-Leone Chen distribution, J. Bus. Environ. Sci., 4 (2025), 106–132.
    [7] E. Altun, D. Bhati, N. M. Khan, A new approach to model the counts of earthquakes: INARPQX(1) process, SN Appl. Sci., 3 (2021), 274. https://doi.org/10.1007/s42452-020-04109-8 doi: 10.1007/s42452-020-04109-8
    [8] R. Alotaibi, E. M. Almetwally, H. Rezk, Reliability analysis of Kavya Manoharan Kumaraswamy distribution under generalized progressive hybrid data, Symmetry, 15 (2023), 1671. https://doi.org/10.3390/sym15091671 doi: 10.3390/sym15091671
    [9] R. Maya, M. R. Irshad, C. Chesneau, S. L. Nitin, D. S. Shibu, On discrete Poisson–Mirra distribution: Regression, INAR (1) process and applications, Axioms, 11 (2022), 193. https://doi.org/10.3390/axioms11050193 doi: 10.3390/axioms11050193
    [10] M. A. Meraou, M. Z. Raqab, F. B. Almathkour, Analyzing insurance data with an alpha power transformed exponential Poisson model, Ann. Data Sci., 2024. https://doi.org/10.1007/s40745-024-00554-z
    [11] M. A. Meraou, N. M. Al-Kandari, M. Z. Raqab, D. Kundu, Analysis of skewed data by using compound Poisson exponential distribution with applications to insurance claims, J. Stat. Comput. Simul., 92 (2021), 928–956. https://doi.org/10.1080/00949655.2021.1981324 doi: 10.1080/00949655.2021.1981324
    [12] M. A. Meraou, N. Al-Kandari, M. Z. Raqab, Univariate and bivariate compound models based on random sum of variates with application to the insurance losses data, J. Stat. Theory Pract., 16 (2022), 56. https://doi.org/10.1007/s42519-022-00282-8 doi: 10.1007/s42519-022-00282-8
    [13] M. A. Meraou, M. Z. Raqab, D. Kundu, F. A. Alqallaf, Inference for compound truncated Poisson log-normal model with application to maximum precipitation data, Comm. Statist. Simulation Comput., 2024. https://doi.org/10.1080/03610918.2024.2328168
    [14] H. Alrweili, E. S. Alotaibi, Bayesian and non-bayesian estimation of Marshall-Olkin XLindley distribution in presence of censoring, cure fraction, and application on medical data, Alexandria Eng. J., 112 (2025), 633–646. https://doi.org/10.1016/j.aej.2024.10.108 doi: 10.1016/j.aej.2024.10.108
    [15] H. Alrweili, Analysis of recent decade rainfall data with new exponential-exponential distribution: Inference and applications, Alexandria Eng. J., 95 (2024), 306–320. https://doi.org/10.1016/j.aej.2024.03.075 doi: 10.1016/j.aej.2024.03.075
    [16] H. Alrweili, On the analysis of environmental and engineering data using alpha power transformed cosine moment exponential model, Int. J. Anal. Appl., 22 (2024), 99. https://doi.org/10.28924/2291-8639-22-2024-99 doi: 10.28924/2291-8639-22-2024-99
    [17] Z. M. Nofal, E. Altun, A. Z. Afify, M. Ahsanullah, The generalized kumaraswamy-G family of distributions, J. Stat. Theory Appl., 18 (2019), 329–342. https://doi.org/10.2991/jsta.d.191030.001 doi: 10.2991/jsta.d.191030.001
    [18] C. D. Obi, P. O. Chukwuma, P. Chinyere, C. P. Igbokwe, P. O. Ibeakuzie, I. C. Anabike, A novel extension of Rayleigh distribution: Characterization, estimation, simulations and applications, J. Xidian Univ., 18 (2024), 177–188. https://doi.org/10.5281/Zenodo.12664617 doi: 10.5281/Zenodo.12664617
    [19] M. Jallal, A. Ahmad, R. Tripathi, Weibull-Power Rayleigh distribution with applications related to distinct fields of science, Realibilty Theory Appl., 2 (2022), 272–290.
    [20] A. Aijaz, S. Q. ul-Ain, A. Afaq, T. Rajnee, Inverse Weibull-Rayleigh distribution characterisation with applications related to cancer data, Reliab. Theory Appl., 16 (2021), 364–382.
    [21] H. Abdulsalam, Y. Abubakar, G. DikkoH, On the properties and applications of a new extension of exponentiated Rayleigh distribution, FUDMA J. Sci., 5 (2021), 377–398. https://doi.org/10.33003/fjs-2021-0502-459 doi: 10.33003/fjs-2021-0502-459
    [22] M. Javed, S. M. Asim, A. Khalil, S. F. Shah, A. Zahra, New Rayleigh flexible Weibull extension (RFWE) distribution with applications to real and simulated data, Model. Simul. Eng., 2022 (2022), 7718284. https://doi.org/10.1155/2022/7718284 doi: 10.1155/2022/7718284
    [23] B. C. Arnold, N. Balskrishnan, H. N. Nagaraja, A first course in order statistics, In: Classics in applied mathematics, Society for Industrial and Applied Mathematics, 2008.
    [24] H. A. David, H. N. Nagaraja, Order statistics, John Wiley & Sons, Inc., 2004.
    [25] M. N. Atchadé, A. A. Agbahide, T. Otodji, M. J. Bogninou, A. M. Djibril, A new shifted Lomax-X family of distributions: Properties and applications to actuarial and financial data, Comput. J. Math. Stat. Sci., 4 (2025), 41–71. http://dx.doi.org/10.21608/cjmss.2024.307114.1066 doi: 10.21608/cjmss.2024.307114.1066
    [26] M. Kamal, R. Aldallal, S. G. Nassr, A. Al Mutairi, M. Yusuf, M. S. Mustafa, et al., A new improved form of the Lomax model: Its bivariate extension and an application in the financial sector, Alexandria Eng. J., 75 (2023), 127–138. https://doi.org/10.1016/j.aej.2023.05.027 doi: 10.1016/j.aej.2023.05.027
    [27] H. Yu, Z. Shang, Z. Wang, Analysis of the current situation of the construction industry in Saudi Arabia and the factors affecting It: An empirical study, Sustainability, 16 (2024), 6756. https://doi.org/10.3390/su16166756 doi: 10.3390/su16166756
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