In this work, estimating the exponentiated half logistic skew-t model parameters using some classical estimation procedures is considered. The finite sample performance of the EHLST parameter estimates is examined through extensive Monte Carlo simulations. The ordering performance of the six criterions was based on the partial and overall ranks of the estimation procedures for all parameter combinations. The criterions performance ordering from finest to poorest, using the overall ranks is maximum likelihood, maximum product of spacing, Anderson-Darling, Cramer-von Mises, least squares and weighted least squares estimators for all the parameter combinations. The simulation results confirm the dominance of the maximum likelihood estimation method over other methods with the least overall rank but shows that the maximum product of spacing is most advantageous when the sample size is 200. More so, the EHLST model efficacy is demonstrated through its application on Nigeria inflation rates dataset using the maximum likelihood and maximum product of spacing estimation procedures. Furthermore, the volatility modeling of the Nigeria inflation log-returns using the GARCH-type models with the EHLST innovation density relative to ten commonly used innovation densities validates the superiority of the GARCH (1, 1) and GJRGARCH (1, 1) models with EHLST innovation density in both in-sample and out-samples performance over other models.
Citation: Obinna D. Adubisi, Ahmed Abdulkadir, Chidi. E. Adubisi. A new hybrid form of the skew-t distribution: estimation methods comparison via Monte Carlo simulation and GARCH model application[J]. Data Science in Finance and Economics, 2022, 2(2): 54-79. doi: 10.3934/DSFE.2022003
[1] | Jagan Mohan Jonnalagadda . On a nabla fractional boundary value problem with general boundary conditions. AIMS Mathematics, 2020, 5(1): 204-215. doi: 10.3934/math.2020012 |
[2] | Lakhdar Ragoub, J. F. Gómez-Aguilar, Eduardo Pérez-Careta, Dumitru Baleanu . On a class of Lyapunov's inequality involving λ-Hilfer Hadamard fractional derivative. AIMS Mathematics, 2024, 9(2): 4907-4924. doi: 10.3934/math.2024239 |
[3] | Wei Zhang, Jifeng Zhang, Jinbo Ni . New Lyapunov-type inequalities for fractional multi-point boundary value problems involving Hilfer-Katugampola fractional derivative. AIMS Mathematics, 2022, 7(1): 1074-1094. doi: 10.3934/math.2022064 |
[4] | Jaganmohan Jonnalagadda, Basua Debananda . Lyapunov-type inequalities for Hadamard type fractional boundary value problems. AIMS Mathematics, 2020, 5(2): 1127-1146. doi: 10.3934/math.2020078 |
[5] | Shuqin Zhang, Lei Hu . The existence of solutions and generalized Lyapunov-type inequalities to boundary value problems of differential equations of variable order. AIMS Mathematics, 2020, 5(4): 2923-2943. doi: 10.3934/math.2020189 |
[6] | Dumitru Baleanu, Muhammad Samraiz, Zahida Perveen, Sajid Iqbal, Kottakkaran Sooppy Nisar, Gauhar Rahman . Hermite-Hadamard-Fejer type inequalities via fractional integral of a function concerning another function. AIMS Mathematics, 2021, 6(5): 4280-4295. doi: 10.3934/math.2021253 |
[7] | Jonas Ogar Achuobi, Edet Peter Akpan, Reny George, Austine Efut Ofem . Stability analysis of Caputo fractional time-dependent systems with delay using vector lyapunov functions. AIMS Mathematics, 2024, 9(10): 28079-28099. doi: 10.3934/math.20241362 |
[8] | Chantapish Zamart, Thongchai Botmart, Wajaree Weera, Prem Junsawang . Finite-time decentralized event-triggered feedback control for generalized neural networks with mixed interval time-varying delays and cyber-attacks. AIMS Mathematics, 2023, 8(9): 22274-22300. doi: 10.3934/math.20231136 |
[9] | Yitao Yang, Dehong Ji . Properties of positive solutions for a fractional boundary value problem involving fractional derivative with respect to another function. AIMS Mathematics, 2020, 5(6): 7359-7371. doi: 10.3934/math.2020471 |
[10] | Tingting Guan, Guotao Wang, Haiyong Xu . Initial boundary value problems for space-time fractional conformable differential equation. AIMS Mathematics, 2021, 6(5): 5275-5291. doi: 10.3934/math.2021312 |
In this work, estimating the exponentiated half logistic skew-t model parameters using some classical estimation procedures is considered. The finite sample performance of the EHLST parameter estimates is examined through extensive Monte Carlo simulations. The ordering performance of the six criterions was based on the partial and overall ranks of the estimation procedures for all parameter combinations. The criterions performance ordering from finest to poorest, using the overall ranks is maximum likelihood, maximum product of spacing, Anderson-Darling, Cramer-von Mises, least squares and weighted least squares estimators for all the parameter combinations. The simulation results confirm the dominance of the maximum likelihood estimation method over other methods with the least overall rank but shows that the maximum product of spacing is most advantageous when the sample size is 200. More so, the EHLST model efficacy is demonstrated through its application on Nigeria inflation rates dataset using the maximum likelihood and maximum product of spacing estimation procedures. Furthermore, the volatility modeling of the Nigeria inflation log-returns using the GARCH-type models with the EHLST innovation density relative to ten commonly used innovation densities validates the superiority of the GARCH (1, 1) and GJRGARCH (1, 1) models with EHLST innovation density in both in-sample and out-samples performance over other models.
The well-known classical Lyapunov inequality [15] states that, if u is a nontrivial solution of the Hill's equation
u′′(t)+q(t)u(t)=0, a<t<b, | (1.1) |
subject to Dirichlet-type boundary conditions:
u(a)=u(b)=0, | (1.2) |
then
∫ba|q(t)|dt>4b−a, | (1.3) |
where q:[a,b]→R is a real and continuous function.
Later, in 1951, Wintner [24], obtained the following inequality:
∫baq+(t)dt>4b−a, | (1.4) |
where q+(t)=max{q(t),0}.
A more general inequality was given by Hartman and Wintner in [12], that is known as Hartman Wintner-type inequality:
∫ba(t−a)(b−t)q+(t)dt>b−a, | (1.5) |
Since maxt∈[a,b](t−a)(b−t)=(b−a)24, then, (1.5) implies (1.4).
The Lyapunov inequality and its generalizations have many applications in different fields such in oscillation theory, asymptotic theory, disconjugacy, eigenvalue problems.
Recently, many authors have extended the Lyapunov inequality (1.3) for fractional differential equations [1,2,3,4,5,6,7,8,9,10,11,12,13,15,18,20,22,23,24]. For this end, they substituted the ordinary second order derivative in (1.1) by a fractional derivative or a conformable derivative. The first result in which a fractional derivative is used instead of the ordinary derivative in equation (1.1), is the work of Ferreira [6]. He considered the following two-point Riemann-Liouville fractional boundary value problem
Dαa+u(t)+q(t)u(t)=0, a<t<b, 1<α≤2 |
u(a)=u(b)=0. |
And obtained the Lyapunov inequality:
∫ba|q(t)|dt>Γ(α)(4b−a)α−1. |
Then, he studied in [7], the Caputo fractional differential equation
CDαa+u(t)+q(t)u(t)=0, a<t<b, 1<α≤2 |
under Dirichlet boundary conditions (1.2). In this case, the corresponding Lyapunov inequality has the form
∫ba|q(t)|dt>ααΓ(α)((α−1)(b−a))α−1. |
Later Agarwal and Özbekler in [1], complimented and improved the work of Ferreira [6]. More precisely, they proved that if u is a nontrivial solution of the Riemann-Liouville fractional forced nonlinear differential equations of order α∈(0,2]:
Dαa+u(t)+p(t)|u(t)|μ−1u(t)+q(t)|u(t)|γ−1u(t)=f(t), a<t<b, |
satisfying the Dirichlet boundary conditions (1.2), then the following Lyapunov type inequality
(∫ba[p+(t)+q+(t)]dt)(∫ba[μ0p+(t)+γ0q+(t)+|f(t)|]dt)>42α−3Γ2(α)(b−a)2α−2. |
holds, where p, q, f are real-valued functions, 0<γ<1<μ<2, μ0=(2−μ)μμ/(2−μ)22/(μ−2) and γ0=(2−γ)γγ/(2−γ)22/(γ−2).
In 2017, Guezane-Lakoud et al. [11], derived a new Lyapunov type inequality for a boundary value problem involving both left Riemann-Liouville and right Caputo fractional derivatives in presence of natural conditions
−CDαb−Dβa+u(t)+q(t)u(t)=0, a<t<b, 0<α,β≤1 |
u(a)=Dβa+u(b)=0, |
then, they obtained the following Lyapunov inequality:
∫ba|q(t)|dt>(α+β−1)Γ(α)Γ(β)(b−a)α+β−1. |
Recently, Ferreira in [9], derived a Lyapunov-type inequality for a sequential fractional right-focal boundary value problem
CDαa+Dβa+u(t)+q(t)u(t)=0, a<t<b |
u(a)=Dγa+u(b)=0, |
where 0<α,β,γ≤1, 1<α+β≤2, then, they obtained the following Lyapunov inequality:
∫ba(b−s)α+β−γ−1|q(t)|dt>1C, |
where
C=(b−a)γmax{Γ(β−γ+1)Γ(α+β−γ)Γ(β+1),1−αβΓ(α+β)(Γ(β−γ+1)Γ(α+β−1)Γ(α+β−γ)Γ(β))α+β−1α−1, with α<1} |
Note that more generalized Lyapunov type inequalities have been obtained for conformable derivative differential equations in [13]. For more results on Lyapunov-type inequalities for fractional differential equations, we refer to the recent survey of Ntouyas et al. [18].
In this work, we obtain Lyapunov type inequality for the following mixed fractional differential equation involving both right Caputo and left Riemann-Liouville fractional derivatives
−CDαb−Dβa+u(t)+q(t)u(t)=0, a<t<b, | (1.6) |
satisfying the Dirichlet boundary conditions (1.2), here 0<β≤α≤1, 1<α+β≤2, CDαb− denotes right Caputo derivative, Dβa+ denotes the left Riemann-Liouville and q is a continuous function on [a,b].
So far, few authors have considered sequential fractional derivatives, and some Lyapunov type inequalities have been obtained. In this study, we place ourselves in a very general context, in that in each fractional operator, the order of the derivative can be different. Such problems, with both left and right fractional derivatives arise in the study of Euler-Lagrange equations for fractional problems of the calculus of variations [2,16,17]. However, the presence of a mixed left and right Caputo or Riemann-Liouville derivatives of order 0<α<1 leads to great difficulties in the study of the properties of the Green function since in this case it's given as a fractional integral operator.
We recall the concept of fractional integral and derivative of order p>0. For details, we refer the reader to [14,19,21]
The left and right Riemann-Liouville fractional integral of a function g are defined respectively by
Ipa+g(t)=1Γ(p)∫tag(s)(t−s)1−pds,Ipb−g(t)=1Γ(p)∫btg(s)(s−t)1−pds. |
The left and right Caputo derivatives of order p>0, of a function g are respectively defined as follows:
CDpa+g(t)=In−pa+g(n)(t),CDpb−g(t)=(−1)nIn−pb−g(n)(t), |
and the left and right Riemann-Liouville fractional derivatives of order p>0, of a function g\ are respectively defined as follows:
Dpa+g(t)=dndtn(In−pa+g)(t),Dpb−g(t)=(−1)ndndtnIn−pb−g(t), |
where n is the smallest integer greater or equal than p.
We also recall the following properties of fractional operators. Let 0<p<1, then:
1- IpCa+Dpa+f(t)=f(t)−f(a).
2- IpCb−Dpb−f(t)=f(t)−f(b).
3- (Ipa+c)(t)=c(t−a)pΓ(p+1),c∈R
4- Dpa+u(t)=CDpa+u(t), when u(a)=0.
5- Dpb−u(t)=CDpb−u(t), when u(b)=0.
Next we transform the problem (1.6) with (1.2) to an equivalent integral equation.
Lemma 1. Assume that 0<α,β≤1. The function u is a solution to the boundary value problem (1.6) with (1.2) if and only if u satisfies the integral equation
u(t)=∫baG(t,r)q(r)u(r)dr, | (2.1) |
where
G(t,r)=1Γ(α)Γ(β)(∫inf{r,t}a(t−s)β−1(r−s)α−1ds |
−(t−a)β(b−a)β∫ra(b−s)β−1(r−s)α−1ds) | (2.2) |
is the Green's function of problem (1.6) with (1.2).
Proof. Firstly, we apply the right side fractional integral Iαb− to equation (1.6), then the left side fractional integral Iβa+ to the resulting equation and taking into account the properties of Caputo and\Riemann-Liouville fractional derivatives and the fact that Dβa+u(t)=CDβa+u(t), we get
u(t)=Iβa+Iαb−q(t)u(t)+c(t−a)βΓ(β+1). | (2.3) |
In view of the boundary condition u(b)=0, we get
c=−Γ(β+1)(b−a)βIβa+Iαb−q(t)u(t)∣t=b. |
Substituting c in (2.3), it yields
u(t)=Iβa+Iαb−q(t)u(t)−(t−a)β(b−a)βIβa+Iαb−q(t)u(t)∣t=b=1Γ(α)Γ(β)∫ta(t−s)β−1(∫bs(r−s)α−1q(r)u(r)dr)ds−(t−a)βΓ(α)Γ(β)(b−a)β∫ba(b−s)β−1(∫bs(r−s)α−1q(r)u(r)dr)ds. |
Finally, by exchanging the order of integration, we get
u(t)=1Γ(α)Γ(β)∫ta(∫ra(t−s)β−1(r−s)α−1ds)q(r)u(r)dr+1Γ(α)Γ(β)∫bt(∫ta(t−s)β−1(r−s)α−1ds)q(r)u(r)dr−(t−a)βΓ(α)Γ(β)(b−a)β∫ba(∫ra(b−s)β−1(r−s)α−1ds)q(r)u(r)dr, |
thus
u(t)=∫baG(t,r)q(r)u(r)dr, |
with
G(t,r)=1Γ(α)Γ(β){∫ra(t−s)β−1(r−s)α−1ds−(t−a)β(b−a)β∫ra(b−s)β−1(r−s)α−1ds,a≤r≤t≤b,∫ta(t−s)β−1(r−s)α−1ds−(t−a)β(b−a)β∫ra(b−s)β−1(r−s)α−1ds,a≤t≤r≤b. |
that can be written as
G(t,r)=1Γ(α)Γ(β)(∫inf{r,t}a(t−s)β−1(r−s)α−1ds−(t−a)β(b−a)β∫ra(b−s)β−1(r−s)α−1ds). |
Conversely, we can verify that if u satisfies the integral equation (2.1), then u is a solution to the boundary value problem (1.6) with (1.2). The proof is completed.
In the next Lemma we give the property of the Green function G that will be needed in the sequel.
Lemma 2. Assume that 0<β≤α≤1,1<α+β≤2, then the Green function G(t,r) given in (2.2) of problem (1.6) with (1.2) satisfies the following property:
|G(t,r)|≤1Γ(α)Γ(β)(α+β−1)(α+β)(α(b−a)(β+α))α+β−1, |
for all a≤r≤t≤b.
Proof. Firstly, for a≤r≤t≤b, we have G(t,r)≥0. In fact, we have
G(t,r)=1Γ(α)Γ(β)(∫ra(t−s)β−1(r−s)α−1ds−(t−a)β(b−a)β∫ra(b−s)β−1(r−s)α−1ds)≥1Γ(α)Γ(β)(∫ra(b−s)β−1(r−s)α−1ds−(t−a)β(b−a)β∫ra(b−s)β−1(r−s)α−1ds) |
=1Γ(α)Γ(β)(1−(t−a)β(b−a)β)∫ra(b−s)β−1(r−s)α−1ds≥0 | (2.4) |
in addition,
G(t,r)≤1Γ(α)Γ(β)(∫ra(r−s)β−1(r−s)α−1ds−(r−a)β(b−a)β∫ra(b−s)β−1(r−s)α−1ds)≤1Γ(α)Γ(β)((r−a)α+β−1(α+β−1)−(r−a)β(b−a)β∫ra(b−a)β−1(r−s)α−1ds) |
=1Γ(α)Γ(β)((r−a)α+β−1(α+β−1)−(r−a)β+αα(b−a)). | (2.5) |
Thus, from (2.4) and (2.5), we get
0≤G(t,r)≤h(r), a≤r≤t≤b, | (2.6) |
where
h(s):=1Γ(α)Γ(β)((s−a)α+β−1(α+β−1)−(s−a)β+αα(b−a)), |
it is clear that h(s)≥0, for all s∈[a,b].
Now, for a≤t≤r≤b, we have
G(t,r)=1Γ(α)Γ(β)(∫ta(t−s)β−1(r−s)α−1ds−(t−a)β(b−a)β∫ra(b−s)β−1(r−s)α−1ds)≤1Γ(α)Γ(β)(∫ta(t−s)β−1(t−s)α−1ds−(t−a)β(b−a)∫ra(r−s)α−1ds)=1Γ(α)Γ(β)((t−a)α+β−1(α+β−1)−(t−a)β(r−a)αα(b−a)) |
≤1Γ(α)Γ(β)((t−a)α+β−1(α+β−1)−(t−a)β+αα(b−a))=h(t). | (2.7) |
On the other hand,
G(t,r)≥1Γ(α)Γ(β)(r−a)α−1∫ta(t−s)β−1ds−(t−a)β(b−a)β∫ra(r−s)β−1(r−s)α−1ds)≥1Γ(α)Γ(β)((t−a)α(t−a)ββ(b−a)−(t−a)β(b−a)β(r−a)α+β−1(α+β−1))≥1Γ(α)Γ(β)((t−a)α+ββ(b−a)−(t−a)β(r−a)α−1(α+β−1))≥1Γ(α)Γ(β)((t−a)α+ββ(b−a)−(t−a)α+β−1(α+β−1)), |
since β≤α, we get
G(t,r)≥−h(t), a≤t≤r≤b. | (2.8) |
From (2.7) and (2.8) we obtain
|G(t,r)|≤h(t), a≤t≤r≤b. | (2.9) |
Finally, by differentiating the function h, it yields
h′(s)=1Γ(α)Γ(β)(s−a)α+β−2(1−(β+α)(s−a)α(b−a)). |
We can see that h′(s)=0 for s0=a+α(b−a)(β+α)∈(a,b), h′(s)<0 for s>s0 and h′(s)>0 for s<s0. Hence, the function h(s) has a unique maximum given by
maxs∈[a,b]h(s)=h(s0)=1Γ(α)Γ(β)((α(b−a)(β+α))α+β−1(α+β−1)−(α(b−a)(β+α))β+αα(b−a))=1Γ(α)Γ(β)(α+β−1)(α+β)(α(b−a)(β+α))α+β−1. |
From (2.6) and (2.9), we get |G(t,r)|≤h(s0), from which the intended result follows.
Next, we state and prove the Lyapunov type inequality for problem (1.6) with (1.2).
Theorem 3. Assume that 0<β≤α≤1 and 1<α+β≤2. If the fractional boundary value problem (1.6) with (1.2) has a nontrivial continuous solution, then
∫ba|q(r)|dr≥Γ(α)Γ(β)(α+β−1)(α+β)α+β(α(b−a))α+β−1. | (2.10) |
Proof. Let X=C[a,b] be the Banach space endowed with norm ||u||=maxt∈[a,b]|u(t)|. It follows from Lemma 1 that a solution u∈X to the boundary value problem (1.6) with (1.2) satisfies
|u(t)|≤∫ba|G(t,r)||q(r)||u(r)|dr≤‖u‖∫ba|G(t,r)|q(r)dr, |
Now, applying Lemma 2 to equation (2.1), it yields
|u(t)|≤1Γ(α)Γ(β)(α+β−1)(α+β)(α(b−a)(β+α))α+β−1‖u‖∫ba|q(r)|dr |
Hence,
‖u‖≤(α(b−a))α+β−1Γ(α)Γ(β)(α+β−1)(α+β)α+β‖u‖∫ba|q(r)|dr, |
from which the inequality (2.10) follows. Note that the constant in (2.10) is not sharp. The proof is completed.
Remark 4. Note that, according to boundary conditions (1.2), the Caputo derivatives CDαb− and CDβa+ coincide respectively with the Riemann-Liouville derivatives Dαb− and Dβa+. So, equation (1.6) is reduced to the one containing only Caputo derivatives or only Riemann-Liouville derivatives, i.e.,
−CDαCb−Dβa+u(t)+q(t)u(t)=0, a<t<b |
or
−Dαb−Dβa+u(t)+q(t)u(t)=0, a<t<b |
Furthermore, by applying the reflection operator (Qf)(t)=f(a+b−t) and taking into account that QCDαa+=CDαb−Q and QCDβb−=CDβa+Q (see [21]), we can see that, the boundary value problem (1.6) with (1.2) is equivalent to the following problem
−CDαa+Dβb−u(t)+q(t)u(t)=0, a<t<b, |
u(a)=u(b)=0. |
Remark 5. If we take α=β=1, then the Lyapunov type inequality (2.3) is reduced to
∫ba|q(t)|dt≥4b−a. |
The authors thank the anonymous referees for their valuable comments and suggestions that improved this paper.
All authors declare no conflicts of interest in this paper.
[1] |
Aas K, Haff IH (2006) The Generalised Hyperbolic Skew Student's t-distribution. J Financ Econ 4: 275-309. https://doi.org/10.1093/jjfinec/nbj006 doi: 10.1093/jjfinec/nbj006
![]() |
[2] |
Adubisi OD, Abdulkadir A, Chiroma H (2021a) A Two Parameter Odd Exponentiated Skew-t Distribution with J-Shaped Hazard Rate Function. J Stat Model Anal 3: 26-46. https://doi.org/10.22452/josma.vol3no1.3 doi: 10.22452/josma.vol3no1.3
![]() |
[3] |
Adubisi OD, Abdulkadir A, Chiroma H, et al. (2021b) The Type I Half Logistic Skew-t Distribution: A Heavy-Tail Model with Inverted Bathtub Shaped Hazard Rate. Asian J Probab Stat 14: 21-40. https://doi.org/10.9734/AJPAS/2021/v14i430336 doi: 10.9734/AJPAS/2021/v14i430336
![]() |
[4] | Adubisi OD, Abdulkadir A, Farouk UA, et al. (2021c) Financial data and a new generalization of the skew-t distribution. Covenant J Phys Life Sci 9: 1-18. |
[5] |
Aldahlan MAD, Afify AM (2020) The odd exponential half-logistic exponential distribution: Estimation methods and application to Engineering data. Mathematics 8: 1684. https://doi.org/10.3390/math8101684 doi: 10.3390/math8101684
![]() |
[6] |
Altun E, (2019) Two-sided exponential-geometric distribution: Inference and volatility modeling. Comput Stat 34: 1215-1245. https://doi.org/10.1007/s00180-019-00873-3 doi: 10.1007/s00180-019-00873-3
![]() |
[7] |
Altun E, Tatlidil H, Ozel G, et al. (2018) A new generalized of skew-T distribution with volatility models. J Stat Comput Simu 88: 1252-1272. https://doi.org/10.1080/00949655.2018.1427240 doi: 10.1080/00949655.2018.1427240
![]() |
[8] |
Alzaatreh A, Lee C, Famoye F (2013) A new method for generating families of continuous distributions. Metron 71: 63-79. https://doi.org/10.1007/s40300-013-0007-y doi: 10.1007/s40300-013-0007-y
![]() |
[9] |
Azzalini A, Capitanio A (2003) Distributions generated by perturbation of symmetry with emphasis on a multivariate skew-t distribution. J Roy Statist Soc B 65: 367-389. https://doi.org/10.1111/1467-9868.00391 doi: 10.1111/1467-9868.00391
![]() |
[10] |
Bakouch HS, Dey S, Ramos PL, et al. (2017) Binomial-exponential 2 distribution: Different estimation methods with weather applications. TEMA 18: 233-251. https://doi.org/10.5540/tema.2017.018.02.0233 doi: 10.5540/tema.2017.018.02.0233
![]() |
[11] |
Basalamah D, Ning W, Gupta A (2018) The beta skew-t distribution and its properties. J Stat Theory Pract 12: 837-860. https://doi.org/10.1080/15598608.2018.1481468 doi: 10.1080/15598608.2018.1481468
![]() |
[12] |
Bollerslev T (1986) Generalized Autoregressive Conditional Heteroskedasticity. J Econometrics 31: 307-327. https://doi.org/10.1016/0304-4076(86)90063-1 doi: 10.1016/0304-4076(86)90063-1
![]() |
[13] |
Brooks C, Burke SP (2003) Information criteria for GARCH model selection. Eur J Financ 9: 557-580. https://doi.org/10.1080/1351847021000029188 doi: 10.1080/1351847021000029188
![]() |
[14] | Cheng R, Amin N (1979) Maximum product of spacing estimation with application to Lognormal distribution. Mathematical Report 79-1, University of Wales, Cardiff, UK. |
[15] |
Cheng R, Amin N (1983) Estimating parameters in continuous univariate distributions with a shifted origin. J R Stat Soc Ser B Methodol 45: 394-403. https://doi.org/10.1111/j.2517-6161.1983.tb01268.x doi: 10.1111/j.2517-6161.1983.tb01268.x
![]() |
[16] |
Chesneau C, Bakouch HS, Ramos PL, et al. (2020) The polynomial-exponential distribution: a continuous probability model allowing for occurrence of zero values. Commun Stat Simul Comput 20: 1-26. https://doi.org/10.1080/03610918.2020.1746339 doi: 10.1080/03610918.2020.1746339
![]() |
[17] |
Cordeiro GM, Alizadeh M, Ortega EMM (2014) The Exponentiated Half-Logistic Family of Distributions: Properties and Applications. J Probab Stat 21 https://doi.org/10.1155/2014/864396 doi: 10.1155/2014/864396
![]() |
[18] | Dikko HG, Agboola S (2017) Exponentiated generalized Skew-t distribution. J Nigerian Assoc Math Phys 42: 219-228. |
[19] |
Engle RF (1982) Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica 50: 987-1008. https://doi.org/10.2307/1912773 doi: 10.2307/1912773
![]() |
[20] |
Glosten LR, Jagannathan R, Runkle DE (1993) On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. J Financ 48: 1779-1801. https://doi.org/10.1111/j.1540-6261.1993.tb05128.x doi: 10.1111/j.1540-6261.1993.tb05128.x
![]() |
[21] | Johnson NL, Kotz S, Balakrishnan N (1995) Continuous Univariate Distributions. New York: Wiley. |
[22] | Jones MC (2001) A skew t distribution. In Probability and Statistical Models with Applications. London: Chapman and Hall, 269-277. |
[23] |
Jones MC, Faddy MJ (2003) A skew extension of the t-distribution, with applications. J Roy Statist Soc Ser B 65: 159-174. https://doi.org/10.1111/1467-9868.00378 doi: 10.1111/1467-9868.00378
![]() |
[24] | Khamis KS, Basalamah D, Ning W, et al. (2017) The Kumaraswamy Skew-t Distribution and Its Related Properties. Commun Stat Simul Comput. https://doi.org/10.1080/03610918.2017.1346801. |
[25] | Louzada F, Ramos PL, Perdoná GS (2016) Different estimation procedures for the parameters of the extended exponential geometric distribution for medical data. Comput Math Methods Med https://doi.org/10.1155/2016/8727951 |
[26] |
Ramos PL, Louzada F, Ramos E, et al. (2020) The Fréchet distribution: Estimation and application-An overview. J Stat Manage Syst 23: 549-578. https://doi.org/10.1080/09720510.2019.1645400 doi: 10.1080/09720510.2019.1645400
![]() |
[27] |
Rodrigues GC, Louzada F, Ramos PL (2018) Poisson-exponential distribution: different methods of estimation. J Appl Stat 45: 128-144. https://doi.org/10.1080/02664763.2016.1268571 doi: 10.1080/02664763.2016.1268571
![]() |
[28] |
Sahu SK, Dey DK, Branco MD (2003) A new class of multivariate skew distributions with applications to Bayesian regression models. Can J Stat 31: 129-150. https://doi.org/10.2307/3316064 doi: 10.2307/3316064
![]() |
[29] |
Shafiei S, Doostparast M (2014) Balakrishnan skew-t distribution and associated statistical characteristics. Comm Statist Theory Methods 43: 4109-4122. https://doi.org/10.1080/03610926.2012.701697 doi: 10.1080/03610926.2012.701697
![]() |
[30] | Shittu OI, Adepoju KA, Adeniji OE (2014) On the Beta Skew-t distribution in modelling stock return in Nigeria. Int J Mod Math Sci 11: 94-102. |
[31] |
ZeinEldin RA, Chesneau C, Jamal F, et al. (2019) Different estimation methods for type I half-logistic Topp-Leone distribution Mathematics 7: 985. https://doi.org/10.3390/math7100985 doi: 10.3390/math7100985
![]() |
![]() |
![]() |
1. | Aidyn Kassymov, Berikbol T. Torebek, Lyapunov-type inequalities for a nonlinear fractional boundary value problem, 2021, 115, 1578-7303, 10.1007/s13398-020-00954-9 | |
2. | Jie Wang, Shuqin Zhang, A Lyapunov-Type Inequality for Partial Differential Equation Involving the Mixed Caputo Derivative, 2020, 8, 2227-7390, 47, 10.3390/math8010047 |