Research article

The modified Kies-Fréchet distribution: Properties, inference and application

  • The present article introduces a new distribution called the modified Kies-Fréchet (MKF) distribution that extends the Fréchet distribution and provides two new sub-models called modified Kies inverse-exponential and modified Kies inverse-Rayleigh distributions. The MKF model can provide left-skewed, symmetrical, right-skewed, J-shaped, and reversed-J shaped densities. The MKF density was expressed as a linear combination of Fréchet densities. We derive some basic mathematical properties of the MKF model. The MKF parameters are estimated using some classical estimation methods called, the maximum likelihood, Anderson-Darling, least-squares, Cramér-von Mises, and weighted least squares. The performances of these estimators were explored by a detailed simulation study. Finally, the flexibility of the MKF model is checked using a real data set, showing that it can provide close fit as compared with other competing models.

    Citation: Mashail M. Al Sobhi. The modified Kies-Fréchet distribution: Properties, inference and application[J]. AIMS Mathematics, 2021, 6(5): 4691-4714. doi: 10.3934/math.2021276

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  • The present article introduces a new distribution called the modified Kies-Fréchet (MKF) distribution that extends the Fréchet distribution and provides two new sub-models called modified Kies inverse-exponential and modified Kies inverse-Rayleigh distributions. The MKF model can provide left-skewed, symmetrical, right-skewed, J-shaped, and reversed-J shaped densities. The MKF density was expressed as a linear combination of Fréchet densities. We derive some basic mathematical properties of the MKF model. The MKF parameters are estimated using some classical estimation methods called, the maximum likelihood, Anderson-Darling, least-squares, Cramér-von Mises, and weighted least squares. The performances of these estimators were explored by a detailed simulation study. Finally, the flexibility of the MKF model is checked using a real data set, showing that it can provide close fit as compared with other competing models.



    The construction of discrete fractional sums and differences from the knowledge of samples of their corresponding continuous integrals and derivatives arises in the context of discrete fractional calculus; see [1,2,3,4,5,6] for more details. Recently, discrete fractional operators with more general forms of their kernels and properties have gathered attention in both areas of physics and mathematics; see [7,8,9,10].

    In discrete fractional calculus theory, we say that F is monotonically increasing at a time step t if the nabla of F is non-negative, i.e., (F)(t):=F(t)F(t1)0 for each t in the time scale set Nc0+1:={c0+1,c0+2,}. Moreover, the function F is θmonotonically increasing (or decreasing) on Nc0 if F(t+1)>θF(t)(orF(t+1)<θF(t)) for each tNa). In [11,12] the authors considered 1monotonicity analysis for standard discrete Riemann-Liouville fractional differences defined on N0 and in [13] the authors generalized the above by introducing θmonotonicity increasing and decreasing functions and then obtained some θmonotonicity analysis results for discrete Riemann-Liouville fractional differences defined on N0. In [14,15,16], the authors considered monotonicity and positivity analysis for discrete Caputo, Caputo-Fabrizio and Attangana-Baleanu fractional differences and in [17,18] the authors considered monotonicity and positivity results for abstract convolution equations that could be specialized to yield new insights into qualitative properties of fractional difference operators. In [19], the authors presented positivity and monotonicity results for discrete Caputo-Fabrizo fractional operators which cover both the sequential and non-sequential cases, and showed both similarities and dissimilarities between the exponential kernel case (that is included in Caputo-Fabrizo fractional operators) and fractional differences with other types of kernels. Also in [20] the authors extended the results in [19] to discrete Attangana-Baleanu fractional differences with Mittag-Leffler kernels. The main theoretical developments of monotonicity and positivity analysis in discrete fractional calculus can be found in [21,22,23,24] for nabla differences, and in [25,26,27,28] for delta differences.

    The main idea in this article is to analyse discrete Caputo-Fabrizo fractional differences with exponential kernels in the Riemann-Liouville sense. The results are based on a notable lemma combined with summation techniques. The purpose of this article is two-fold. First we show the positiveness of discrete fractional operators from a theoretical point of view. Second we shall complement the theoretical results numerically and graphically based on the standard plots and heat map plots.

    The plan of the article is as follows. In Section 2 we present discrete fractional operators and the main lemma. Section 3 analyses the discrete fractional operator in a theoretical sense. In Section 4 we discuss our theoretical strategy on standard plots (Subsection 4.1) and heat map plots (Subsection 4.2). Finally, in Section 5 we summarize our findings.

    First we recall the definitions in discrete fractional calculus; see [2,3,5] for more information.

    Definition 2.1. (see [2,Definition 2.24]). Let c0R, 0<θ1, F be defined on Nc0 and Λ(θ)>0 be a normalization constant. Then the following operator

    (CFRc0θF)(t):=Λ(θ)ttr=c0+1F(r)(1θ)tr{tNc0+1},

    is called the discrete Caputo-Fabrizio fractional operator with exponential kernels in the Riemann-Liouville sense CFR, and the following operator

    (CFCc0θF)(t):=Λ(θ)tr=c0+1(rF)(r)(1θ)tr{tNc0+1},

    is called the discrete Caputo-Fabrizo fractional operator with exponential kernels in the Caputo sense CFC.

    Definition 2.2 (see [3]). For F:Nc0κR with κ<θκ+1 and κN0, the discrete nabla CFC and CFR fractional differences can be expressed as follows:

    (CFCc0θF)(t)=(CFCc0θκκF)(t),

    and

    (CFRc0θF)(t)=(CFRc0θκκF)(t),

    respectively, for each tNc0+1.

    The following lemma is essential later.

    Lemma 2.1. Assume that F is defined on Nc0 and 1<θ<2. Then the CFR fractional difference is

    (CFRc0θF)(t)=Λ(θ1){(F)(t)+(1θ)(2θ)tc02(F)(c0+1)+(1θ)t1r=c0+2(rF)(r)(2θ)tr1},

    for each tNc0+2.

    Proof. From Definitions 2.1 and 2.2, the following can be deduced for 1<θ<2:

    (CFRc0θF)(t)=Λ(θ1){tr=c0+1(rF)(r)(2θ)trt1r=c0+1(rF)(r)(2θ)tr1}=Λ(θ1){(F)(t)+t1r=c0+1(rF)(r)[(2θ)tr(2θ)tr1]}=Λ(θ1){(F)(t)+(1θ)(2θ)tc02(F)(c0+1)+(1θ)t1r=c0+2(rF)(r)(2θ)tr1},

    for each tNc0+2.

    In the following theorem, we will show that F is monotonically increasing at two time steps even if (CFRc0θF)(t) is negative at the two time steps.

    Theorem 3.1. Let the function F be defined on Nc0+1, and let 1<θ<2 and ϵ>0. Assume that

    (CFRc0θF)(t)>ϵΛ(θ1)(F)(c0+1)fort{c0+2,c0+3}s.t.(F)(c0+1)0. (3.1)

    If (1θ)(2θ)<ϵ, then (F)(c0+2) and (F)(c0+3) are both nonnegative.

    Proof. From Lemma 2.1 and condition (3.1) we have

    (F)(t)(F)(c0+1)[(1θ)(2θ)tc02+ϵ](1θ)t1r=c0+2(rF)(r)(2θ)tr1, (3.2)

    for each tNc0+2. At t=c0+2, we have

    (F)(c0+2)(F)(c0+1)[(1θ)+ϵ](1θ)c0+1r=c0+2(rF)(r)(2θ)c0+1r=00,

    where we have used (1θ)<(1θ)(2θ)<ϵ and (F)(c0+1)0 by assumption. At t=c0+3, it follows from (3.2) that

    (F)(c0+3)=(F)(c0+1)[(1θ)(2θ)+ϵ](1θ)c0+2r=c0+2(rF)(r)(2θ)c0+2r=(F)(c0+1)0[(1θ)(2θ)+ϵ]<0(1θ)<0(F)(c0+2)00, (3.3)

    as required. Hence the proof is completed.

    Remark 3.1. It worth mentioning that Figure 1 shows the graph of θ(1θ)(2θ) for θ(1,2).

    Figure 1.  Graph of θ(1θ)(2θ) for θ(1,2).

    In order for Theorem 3.1 to be applicable, the allowable range of ϵ is ϵ(0,(2θ)(1θ)) for a fixed θ(1,2)

    Now, we can define the set Hκ,ϵ as follows

    Hκ,ϵ:={θ(1,2):(1θ)(2θ)κc02<ϵ}(1,2),κNc0+3.

    The following lemma shows that the collection {Hκ,ϵ}κ=c0+1 forms a nested collection of decreasing sets for each ϵ>0.

    Lemma 3.1. Let 1<θ<2. Then, for each ϵ>0 and κNc0+3 we have that Hκ+1,ϵHκ,ϵ.

    Proof. Let θHκ+1,ϵ for some fixed but arbitrary κNc0+3 and ϵ>0. Then we have

    (1θ)(2θ)κc01=(1θ)(2θ)(2θ)κc02<ϵ.

    Considering 1<θ<2 and κNc0+3, we have 0<2θ<1. Consequently, we have

    (1θ)(2θ)κc02<ϵ 12θ>1<ϵ.

    This implies that θHκ,ϵ, and thus Hκ+1,ϵHκ,ϵ.

    Now, Theorem 3.1 and Lemma 3.1 lead to the following corollary.

    Corollary 3.1. Let F be a function defined on Nc0+1, θ(1,2) and

    (CFRc0θF)(t)>ϵΛ(θ1)(F)(c0+1)suchthat(F)(c0+1)0, (3.4)

    for each tNsc0+3:={c0+3,c0+4,,s} and some sNc0+3. If θHs,ϵ, then we have (F)(t)0 for each tNsc0+1.

    Proof. From the assumption θHs,ϵ and Lemma 3.1, we have

    θHs,ϵ=Hs,ϵs1κ=c0+3Hκ,ϵ.

    This leads to

    (1θ)(2θ)tc02<ϵ, (3.5)

    for each tNsc0+3.

    Now we use the induction process. First for t=c0+3 we obtain (F)(c0+3)0 directly as in Theorem 3.1 by considering inequalities (Eq 3.4) and (Eq 3.5) together with the given assumption (F)(c0+1)0. As a result, we can inductively iterate inequality (Eq 3.2) to get

    (F)(t)0,

    for each tNsc0+2. Moreover, (F)(c0+1)0 by assumption. Thus, (F)(t)0 for each tNsc0+1 as desired.

    In this section, we consider the methodology for the positivity of F based on previous observations in Theorem 3.1 and Corollary 3.1 in such a way that the initial conditions are known. Later, we will illustrate other parts of our article via standard plots and heat maps for different values of θ and ϵ. The computations in this section were performed with MATLAB software.

    Example 4.1. Considering Lemma 1 with t:=c0+3:

    (CFRc0θF)(c0+3)=Λ(θ1){(F)(c0+3)+(1θ)(2θ)(F)(c0+1)+(1θ)c0+2r=c0+2(rF)(r)(2θ)c0+2r}.

    For c0=0, it follows that

    (CFR0θF)(3)=Λ(θ1){(F)(3)+(1θ)(2θ)(F)(1)+(1θ)2r=2(rF)(r)(2θ)2r}=Λ(θ1){(F)(3)+(1θ)(2θ)(F)(1)+(1θ)(F)(2)}=Λ(θ1){F(3)F(2)+(1θ)(2θ)[F(1)F(0)]+(1θ)[F(2)F(1)]}.

    If we take θ=1.99,F(0)=0,F(1)=1,F(2)=1.001,F(3)=1.005, and ϵ=0.007, we have

    (CFR01.99F)(3)=Λ(0.99){0.004+(0.99)(0.01)(0)+(0.99)(0.001)}=0.0069Λ(0.99)>0.007Λ(0.99)=ϵΛ(0.99)(F)(1).

    In addition, we see that (1θ)(2θ)=0.0099<0.007=ϵ. Since the required conditions are satisfied, Theorem 3.1 ensures that (F)(3)>0.

    In Figure 2, the sets Hκ,0.008 and Hκ,0.004 are shown for different values of κ, respectively in Figure 2a, b. It is noted that Hκ,0.008 and Hκ,0.004 decrease by increasing the values of κ. Moreover, in Figure 2a, the set Hκ,0.008 becomes empty for κ45; however, in Figure 2b, we observe the non-emptiness of the set Hκ,0.004 for many larger values of κ up to 90. We think that the measures of Hκ,0.008 and Hκ,0.004 are not symmetrically distributed when κ increases (see Figure 2a, b). We do not have a good conceptual explanation for why this symmetric behavior is observed. In fact, it is not clear why the discrete nabla fractional difference CFRc0θ seems to give monotonically when θ1 rather than for θ2, specifically, it gives a maximal information when θ is very close to 1 as ϵ0+.

    Figure 2.  Graph of Hκ,ϵ for different values of κ and ϵ.

    In the next figure (Figure 3), we have chosen a smaller ϵ (ϵ=0.001), we see that the set Hκ,0.001 is non-empty for κ>320. This tells us that small choices in ϵ give us a more widely applicable result.

    Figure 3.  Graph of Hκ,ϵ for κN3503 and ϵ=0.001.

    In this part, we introduce the set Hκ:={κ:θHκ,ϵ} to simulate our main theoretical findings for the cardinality of the set Hκ via heat maps in Figure 4ad. In these figures: we mean the warm colors such as red ones and the cool colors such as blue ones. Moreover, the θ values are on the xaxis and ϵ values are on the yaxis. We choose ϵ in the interval [0.00001, 0.0001]. Then, the conclusion of these figures are as follows:

    Figure 4.  The cardinality of Hκ for different values of θ with 0.00001ϵ0.0001 in heat maps.

    ● In Figure 4a when θ(1,2) and Figure 4b when θ(1,1.5), we observe that the warmer colors are somewhat skewed toward θ very close to 1, and the cooler colors cover the rest of the figures for θ above 1.05.

    ● In Figure 4c, d, the warmest colors move strongly towards the lower values of θ, especially, when θ(1,1.05). Furthermore, when as θ increases to up to 1.0368, it drops sharply from magenta to cyan, which implies a sharp decrease in the cardinality of Hκ for a small values of ϵ as in the interval [0.00001, 0.0001].

    On the other hand, for larger values of ϵ, the set Hκ will tend to be empty even if we select a smaller θ in such an interval (1,1.05). See the following Figure 5a, b for more.

    Figure 5.  The cardinality of Hκ for different values of ϵ with 1<θ<1.05 in heat maps.

    In conclusion, from Figures 4 and 5, we see that: For a smaller value of ϵ, the set Hκ,ϵ tends to remain non-empty (see Figure 4), unlike for a larger value of ϵ (see Figure 5). Furthermore, these verify that Corollary 3.1 will be more applicable for 1<θ<1.05 and 0.01<ϵ<0.1 as shown in Figure 4d.

    Although, our numerical data strongly note the sensitivity of the set Hκ when slight increasing in ϵ is observed for θ close to 2 compares with θ close to 1.

    In this paper we developed a positivity method for analysing discrete fractional operators of Riemann-Liouville type based on exponential kernels. In our work we have found that (F)(3)0 when (CFRc0θF)(t)>ϵΛ(θ1)(F)(c0+1) such that (F)(c0+1)0 and ϵ>0. We continue to extend this result for each value of t in Nsc0+1 as we have done in Corollary 3.1.

    In addition we presented standard plots and heat map plots for the discrete problem that is solved numerically. Two of the graphs are standard plots for Hκ,ϵ for different values of κ and ϵ (see Figure 2), and the other six graphs consider the cardinality of Hκ for different values of ϵ and θ (see Figures 4 and 5). These graphs ensure the validity of our theoretical results.

    In the future we hope to apply our method to other types of discrete fractional operators which include Mittag-Leffler and their extensions in kernels; see for example [5,6].

    This work was supported by the Taif University Researchers Supporting Project Number (TURSP-2020/86), Taif University, Taif, Saudi Arabia.

    The authors declare there is no conflict of interest.



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