
Citation: Daniel Sultanov, Nadezhda Gerasimova, Kseniya Kudryashova, Natalya Maluchenko, Elena Kotova, Marie-France Langelier, John Pascal, Mikhail Kirpichnikov, Alexey Feofanov, Vasily Studitsky. Unfolding of core nucleosomes by PARP-1 revealed by spFRET microscopy[J]. AIMS Genetics, 2017, 4(1): 21-31. doi: 10.3934/genet.2017.1.21
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The use of bivariate distributions is essential in a wide variety of fields since they are used to represent and analyze the relationship between random variables (RVs). A substantial quantity of research is involved in developing bivariate distributions. Morgenstern [31] introduced a straightforward technique for creating a two-variable set of distributions by utilizing marginal values. Farlie [14] introduced a broader version of Morgenstern's approach, referred to as the FGM family of distributions. Several researchers have devised and investigated numerous bivariate distributions. For example, a new bivariate Fréchet distribution based on FGM and Ali-Mikhail-Haq copulas was proposed by Almetwally and Muhammed [4]. Furthermore, based on the FGM copula, Almetwally et al. [5] introduced a bivariate Weibull distribution and obtained some properties of it. Using generalized OSs, Nagy et al. [32] examined the statistical properties of the q-Weibull distribution in univariate and bivariate situations. Recently, Fayomi et al. [15] derived several structural statistical properties for a novel family of bivariate continuous Lomax generators.
Sklar [39] proposed a joint distribution function (JDF) of given two RVs W1 and W2 by using a copula C(u,v), and the two marginals DFs FW1(w1)=P(W1≤w1) and FW2(w2)=P(W2≤w2) as
FW1,W2(w1,w2)=C(FW1(w1),FW2(w2)). | (1.1) |
When the JDF is absolutely continuous, we get
fW1,W2(w1,w2)=fW1(w1)fW2(w2)c(FW1(w1),FW2(w2)), | (1.2) |
where fW1,W2(w1,w2) is the joint probability density function (JPDF) of FW1,W2(w1,w2) and c(u,v) is the PDF of the copula C(u,v) (for more details about copula and the Sklar theorem, see Barakat et al. [9], Iordanov and Chervenov [22], Joe [24], and Nelsen [33].
One of the most widespread and beneficial copula is the FGM copula C(u,v)=uv[1+δ(1−u)(1−v)],0≤u,v≤1,−1≤δ≤1, (cf. Nelsen, [33], Eq 3.2.10). The corresponding bivariate DF is the FGM model
FW1,W2(w1,w2)=FW1(w1)FW2(w2)[1+δˉFW1(w1)ˉFW2(w2)], | (1.3) |
where ˉFWi(wi) is the survival function (or the reliability function ˉFWi(wi)=P(Wi>wi)) of FWi(wi),i=1,2. The PDF of the distribution (1.3) is given by
fW1,W2(w1,w2)=fW1(w1)fW2(w2)[1+δ(2FW1(w1)−1)(2FW2(w2)−1)]. | (1.4) |
Thanks to the simple shape of the FGM copula, this copula provides numerous benefits when modeling bivariate distributions. The FGM copula has its versatility in representing a broad spectrum of dependence structures. Finance, actuarial science, and bioinformatics are a few fields where the FGM copula is used (cf. Teugels, [41]). Furthermore, the FGM copula enables the creation of bivariate distributions that encompass a diverse set of marginals, including both continuous and discrete marginals, cf. Nelsen [33]. In addition, the FGM copula has a straightforward structure, making it computationally efficient and straightforward to implement in practical applications, cf. Joe [24]. The FGM distribution is a flexible and valuable family in applications as long as the correlation between the variables is not too high. Schucany et al. [37] showed that the correlation coefficient, ρ, between the two marginal DFs FW1(w1) and FW2(w2) satisfies −13≤ρ≤13, whenever these margins are continuous distributions with bounded nonzero variances. Additionally, for uniform margins, ρ reaches its maximum, 13.
In statistics, a parsimonious model fits the data well by utilizing a small number of independent variables, as well as a small number of unknown parameters. Models that are parsimony aim towards simplicity. This idea is consistent with Occam's razor, which states that if there are two possible explanations for a given situation, the simplest one is typically correct. The parsimony principle, when used in statistical models, seeks to explain data with the fewest number of parameters while yet offering essential advantages.
Alkhazaalh and Al-Zoubi [6] proposed a new continuous distribution using the Epanechnikov kernel function and the EX distribution. Therefore, this distribution was named the Epanechnikov-exponential distribution and is denoted as the EP-EX distribution. The DF and PDF of the EP-EX distribution are respectively given by
FW(w;φ)=12(e−3φw−3e−2φw)+1,φ>0,w>0, | (1.5) |
and
fW(w;φ)=3φ2(2e−2φw−e−3φw),φ>0,w>0, | (1.6) |
where φ is the scale parameter. The EP-EX distribution is considered a parsimonious model with no shape parameter like the EX distribution. Furthermore, the EX and EP-EX distributions have an analytically straightforward shape. Alkhazaalh and Al-Zoubi [6] demonstrated that the EP-EX distribution (1.5) is more adaptable and powerful than the EX distribution through an application to an actual data set. However, theoretically, each of the two distributions can describe groups of various kinds of data sets, meaning that neither is superior to the other because the kurtosis, 7.7734, and skewness, 1.7945, of the EP-EX distribution are smaller than the kurtosis, 9, and skewness, 2, of the EX distribution, respectively. This makes the two distributions complementary to each other rather than competitors. However, the EP-EX distribution has a non-constant hazard function, unlike the EX distribution, which gives the EP-EX distribution an advantage in processing the reliability data. Alkhazaalh and Al-Zoubi [6] showed that the mean, variance, and moment generating function (MGF) of EP-EX distribution are given, respectively, by
μ=E(W)=712φ,Var(W)=σ2W=43144φ2,andMW(t)=3φ2φ−t−3φ2(3φ−t),φ>t2. |
Two parsimonious and complementary bivariate DFs that may describe different kinds of bivariate data sets are obtained using the FGM copula and the EX and EP-EX marginals. Each of these models has a single shape parameter, δ. We concentrate on the second model with the EP-EX marginals here because the first model with the EX marginals has been well-examined in the literature (e.g., see EX-FGM as a special case of Weibull-FGM bivariate DF introduced by Almetwally et al. [5]). We examine this model from several statistical angles and information theory.
The paper is organized in the following manner. Sections 2 and 3 provide a detailed description of the distributional characteristics of the EP-EX-FGM model. Furthermore, Section 4 explores the reliability measures, such as the hazard function, reversed hazard (RH) function, and positive quadrant dependence feature. Moreover, recent information measures such as bivariate extropy, bivariate weighted extropy, and bivariate cumulative residual extropy (bivariate CREX) are evaluated. Section 5 employs the maximum likelihood (ML) and Bayesian techniques to estimate the parameters of the model. Furthermore, the model's parameter is calculated using confidence intervals based on asymptotic methods. In Section 6, Monte Carlo simulations were employed to approximate the ML and Bayesian estimators. The evaluation of bivariate real-world data sets in Section 7 produced outstanding outcomes. Section 8 serves as the conclusion of the paper.
Let W1∼EP-EX(φ1) and W2∼EP-EX(φ2). Thus, according to (1.1), the JDF of bivariate EP-EX based on FGM copula, denoted by EP-EX-FGM(φ1;φ2), is given by
FW1,W2(w1,w2)=(12(e−3φ1w1−3e−2φ1w1)+1)(12(e−3φ2w2−3e−2φ2w2)+1)[1+δ11×(1−(12(e−3φ1w1−3e−2φ1w1)+1))(1−(12(e−3φ2w2−3e−2φ2w2)+1))]. | (2.1) |
Moreover, according to (1.2), the corresponding JPDF of the JDF (2.1) is given by
fW1,W2(w1,w2)=9φ1φ24(2e−2φ1w1−e−3φ1w1)(2e−2φ2w2−e−3φ2w2)[1+δ11×(1−2(12(e−3φ1w1−3e−2φ1w1)+1))(1−2(12(e−3φ2w2−3e−2φ2w2)+1))]. | (2.2) |
The 3D Figure 1 illustrates the JPDF for specific parameter values. The graphs depicted in Figure 1 illustrate the remarkable wealth and versatility of this family since it is capable of handling several forms of data.
It is easy to show that the (s1,s2)th, s1,s2=1,2,..., product moments of the EP-EX-FGM(φ1; φ2) are given by
E(Ws11Ws22)=9φ1φ24∫∞0∫∞0ws11ws22(2e−2φ1w1−e−3φ1w1)(2e−2φ2w2−e−3φ2w2)[1+δ11×(1−2(12(e−3φ1w1−3e−2φ1w1)+1))(1−2(12(e−3φ2w2−3e−2φ2w2)+1))]dw1dw2=9Γ(1+s1)Γ(1+s2)4φs11φs22[(2−s1−3−s1−1)(2−s2−3−s2−1)+δ(6−s1−1(1+2s1+1)+2−s1(3×2−s1−1−1)−5−s1)(6−s2−1(1+2s2+1)+2−s2(3×2−s2−1−1)−5−s2)]. | (3.1) |
Thus, by using (3.1) at s1=s2=1, we get
E(W1W2)=49144φ1φ2[1+δ448919600]. |
Therefore, the coefficient of correlation between W1 and W2 is
ρW1,W2=448917200δ≈0.261δ. |
We notice that ρW1,W2=0 when δ=0, which implies that W1 and W2 are independent. The maximum and minimum values of ρW1,W2 from EP-EX-FGM(φ1; φ2) are 0.261 and −0.261, respectively.
By using (2.2), the MGF of W1 and W2 is given by
MW1,W2(r1,r2)=∞∑i=0ri1i!∞∑j=0rj2j!9Γ(1+i)Γ(1+j)4φi1φj2[(2−i−3−i−1)(2−j−3−j−1)+δ(6−i−1(1+2i+1)+2−i(3×2−i−1−1)−5−i)(6−j−1(1+2j+1)+2−j(3×2−j−1−1)−5−j)]. |
After simple algebra, the conditional PDF and DF of W2 given W1=w1 are respectively given by
fW2|W1(w2|w1)=3φ22(2e−2φ2w2−e−3φ2w2)[1+δ(1−2(12(e−3φ1w1−3e−2φ1w1)+1))×(1−2(12(e−3φ2w2−3e−2φ2w2)+1))], |
and
FW2|W1(w2|w1)=(12(e−3φ2w2−3e−2φ2w2)+1)[1+δ(1−2(12(e−3φ1w1−3e−2φ1w1)+1))×(1−(12(e−3φ2w2−3e−2φ2w2)+1))]. |
Consequently, for the EP-EX-FGM(φ1; φ2), the regression curve for W2 given W1=w1 is
E(W2|W1=w1)=712φ2[1−67δ140(1−2(12(e−3φ1w1−3e−2φ1w1)+1))]. |
Thus, the conditional expectation is non-linear with respect to w1.
The concomitants are a vital tool when selection and prediction problems are involved. The idea of concomitants of OSs was first proposed by David [12]. Many studies have been published on the concomitants of the OS model. Researchers such as Abd Elgawad et al. [2], Barakat et al. [8,10], and Scaria and Nair [36] have studied this issue.
Let (W1i,W2i),i=1,2,...,n, be a random sample from a continuous bivariate DF FW1,W2(w1,w2). If we denote W1r:n as the rth OS of the W1 sample values, then the W2 values associated with W1r:n are called the concomitants of the rth OS and are denoted by W2[r:n],r=1,2,...,n. The PDF of the concomitant of the rth OS is given by
f2[r:n](w2)=∫∞−∞fW2|W1(w2|w1)f1r,n(w1)dw1, |
where f1r,n(w1) is the PDF of the rth OS of W1i,i=1,2,...,n. Thus, the PDF of W2[r:n] is given by
f2[r:n](w2)=∫∞w1=0fW2|W1(w2|w1)f1r,n(w1)dw1=∫∞w1=0fW2,W1(w2,w1)fW1(w1)f1r,n(w1)dw1=n!(r−1)!(n−r)!∫∞w1=0fW2,W1(w2,w1)fW1(w1)fW1(w1)[FW1(w1)]r−1[1−FW1(w1)]n−rdw1=9φ1φ24n!(r−1)!(n−r)!∫∞w1=0(2e−2φ1w1−e−3φ1w1)(2e−2φ2w2−e−3φ2w2)[1+δ×(1−2(12(e−3φ1w1−3e−2φ1w1)+1))(1−2(12(e−3φ2w2−3e−2φ2w2)+1))]×(12(e−3φ1w1−3e−2φ1w1)+1)r−1(−12(e−3φ1w1−3e−2φ1w1))n−rdw1=3φ22(2e−2φ2w2−e−3φ2w2)[1+δ(n−2r+1n+1)(1−2(12(e−3φ2w2−3e−2φ2w2)+1))]. |
Also, the MGF of W2[r:n] is given by
MW2[r:n](t)=∫∞w2=03φ22etw2(2e−2φ2w2−e−3φ2w2)×[1+δ(n−2r+1n+1)(1−2(12(e−3φ2w2−3e−2φ2w2)+1))]dw2=32φ2(1t−3φ2−2t−2φ2)−3δφ2(n−2r+1)(1t−3φ2+6t−4φ2−5t−5φ2+1t−6φ2−2t−2φ2)2(n+1). |
The ℓth moment of W2[r:n] is given by
μ(ℓ)[r:n]=∫∞w2=03φ22wℓ2(2e−2φ2w2−e−3φ2w2)×[1+δ(n−2r+1n+1)(1−2(12(e−3φ2w2−3e−2φ2w2)+1))]dw2=2−ℓ−13−ℓ(3ℓ+1−2ℓ)φ−ℓ2Γ(ℓ+1)+4−ℓ−115−ℓ(5ℓ(2ℓ+22ℓ+1+3ℓ+2−6ℓ+1)−22ℓ+13ℓ+1)δφ−ℓ2Γ(ℓ+1)(n−2r+1)n+1. |
The MRL is the average life of a unit after it has survived for a specific amount of time t. The MRL function is like the PDF or the characteristic function for a distribution with a finite mean, the MRL completely determines the distribution via an inversion formula (cf. Guess and Proschan, [17]). The MRL is not only used for parametric modeling, but also for nonparametric modeling. Actuaries apply MRL to set rates and benefits for life insurance. In the biomedical setting, researchers analyze survivorship studies by MRL. Shanbag and Kotz [38] introduced the concept of the MRL for vector-valued RVs as
m(w1,w2)=(m1(w1,w2),m2(w1,w2)), | (3.2) |
where
m1(w1,w2)=E(W1−w1|W1≥w1,W2≥w2) |
and
m2(w1,w2)=E(W2−w2|W1≥w1,W2≥w2). |
The expressions for m1(w1,w2) and m2(w1,w2) in EP-EX-FGM(φ1; φ2) are obtained as
m1(w1,w2)=76φ1(1−67δ140D2)(3e−2φ1w1−e−3φ1w1)[1+δ4(1+D1)(1+D2)] | (3.3) |
and
m2(w1,w2)=76φ2(1−67δ140D1)(3e−2φ2w2−e−3φ2w2)[1+δ4(1+D1)(1+D2)], | (3.4) |
where Dj=(2(12(e−3φjwj−3e−2φjwj)+1)−1), j=1,2. Substituting (3.3) and (3.4) in (3.2) yields EP-EX-FGM's MRL.
The vitality function is a helpful tool for modeling lifetime data. It was thoroughly explored by Kupka and Loo [28] in relation to their research on the aging process. This idea was applied by Kotz and Shanbhag [27] to produce multiple lifetime distribution characterizations. The vitality function offers a more direct assessment of the failure pattern since it is expressed in terms of an increased average lifespan, whereas the hazard rate represents the chance of sudden death within a lifespan. The vitality function linked to a non-negative RV W is defined as m(w)=E(W|W>w). The bivariate vitality function of a random vector (W1,W2) is defined on a positive domain as a binomial vector as
V(w1,w2)=(V1(w1,w2),V2(w1,w2)), | (3.5) |
where
V1(w1,w2)=E(W1|W1≥w1,W2≥w2) |
and
V2(w1,w2)=E(W2|W1≥w1,W2≥w2). |
For more details, see Sankaran and Nair [35]. Also, Vi(w1,w2) is related to mi(w1,w2) by
Vi(w1,w2)=wi+mi(w1,w2),i=1,2. | (3.6) |
Here, V1(w1,w2) computes the expected lifetime of the first component as the sum of current age w1 and the average lifetime remaining for it, assuming the second component has survived past age w2. V2(w1,w2) has a similar interpretation. Using (3.3) and (3.4) in (3.6), we obtain V1(w1,w2) and V2(w1,w2) of EP-EX-FGM as
V1(w1,w2)=76φ1(1−67δ140D2)(3e−2φ1w1−e−3φ1w1)[1+δ4(1+D1)(1+D2)]+w1 | (3.7) |
and
V2(w1,w2)=76φ2(1−67δ140D1)(3e−2φ2w2−e−3φ2w2)[1+δ4(1+D1)(1+D2)]+w2. | (3.8) |
From (3.7) and (3.8), the vitality function of EP-EX-FGM can be obtained using (3.5).
In this section, we derive reliability measures such as hazard function, RH function, MRL, vitality function, positive quadrant dependence feature, bivariate extropy, bivariate weighted extropy, and bivariate CREX in the context of EP-EX-FGM(φ1; φ2). Sreelakshmi [40] introduced the relationship between copula and reliability function, which is defined as follows:
R(w1,w2)=1−FW1(w1)−FW2(w2)+C(FW1(w1),FW2(w2)). |
The reliability function R(w1,w2)=P(W1>w1,W2>w2) for the EP-EX-FGM(φ1; φ2) is
R(w1,w2)=14(3e−2φ1w1−e−3φ1w1)(3e−2φ2w2−e−3φ2w2)[1+δ(12(e−3φ1w1−3e−2φ1w1)+1)×(12(e−3φ2w2−3e−2φ2w2)+1)]. |
The bivariate hazard function at a point (w1,w2) is defined, according to Basu [11], by H(w1,w2)=fW1,W2(w1,w2)R(w1,w2). Thus, we get
H(w1,w2)=9φ1φ2(2e−2φ1w1−e−3φ1w1)(2e−2φ2w2−e−3φ2w2)[1+δD1D2](3e−2φ1w1−e−3φ1w1)(3e−2φ2w2−e−3φ2w2)[1+δ4(1+D1)(1+D2)]. | (4.1) |
One of the key constraints of Basu [11] is that H(w1,w2), as defined by (4.1), is not a vector quantity, as defined by R2→R. The bivariate hazard function was created in vector form by Johnson et al. [25] and Sreelakshmi [40] to get around this restriction.
H(w1,w2)=(−∂lnR(w1,w2)∂w1,−∂lnR(w1,w2)∂w2), | (4.2) |
where R denotes the bivariate reliability function for the FGM copula. For the FGM copula, Vaidyanathan [42] studied the elements in vector (4.2). For the EP-EX-FGM copula, we can, after simple algebra, get the following relations:
−∂lnR(w1,w2)∂w1=3φ1(2e−2φ1w1−e−3φ1w1)(3e−2φ1w1−e−3φ1w1)(1−δ4(3e−2φ1w1−e−3φ1w1)(1+D2)[1+δ4(1+D1)(1+D2)]) | (4.3) |
and
−∂lnR(w1,w2)∂w2=3φ2(2e−2φ2w2−e−3φ2w2)(3e−2φ2w2−e−3φ2w2)(1−δ4(3e−2φ2w2−e−3φ2w2)(1+D1)[1+δ4(1+D1)(1+D2)]). | (4.4) |
The vector hazard function of EP-EX-FGM(φ1; φ2) is obtained by substituting (4.3) and (4.4) in (4.2). Figure 2 depicts 3D plots of the joint hazard function (JHF) of an EP-EX-FGM for various parameter values.
The RH function at a point (w1,w2) is defined as RH(w1,w2)=fW1,W2(w1,w2)FW1,W2(w1,w2). Thus, we get
RH(w1,w2)=9φ1φ2(2e−2φ1w1−e−3φ1w1)(2e−2φ2w2−e−3φ2w2)[1+δD1D2](1+D1)(1+D2)[1+δ4(1−D1)(1−D2)]. |
Our goal in this section is to examine the positive quadrant dependence property, denoted by PQD, (negative quadrant dependence, denoted by NQD) of the RVs W1 and W2 associated with the EP-EX-FGM. As a type of RV dependence, PQD was introduced by Lehmann [30]. This kind of dependence describes the joint behavior of two RVs when they are both large (or small). More specifically, two RVs are PQD if there is at least as much chance that they are both tiny at the same time as there would be if they were independent. The following theorem reveals these properties for the EP-EX-FGM(φ1; φ2).
Theorem 4.1. The EP-EX-FGM(φ1; φ2) is PQD (NQD) for positive (negative) values of δ.
Proof. Consider
P(W1>w1,W2>w2)−P(W1>w1)P(W2>w2)=R(w1,w2)−R(w1)R(w2) |
=14(3e−2φ1w1−e−3φ1w1)(3e−2φ2w2−e−3φ2w2)[1+δ(12(e−3φ1w1−3e−2φ1w1)+1)×(12(e−3φ2w2−3e−2φ2w2)+1)]−14(3e−2φ1w1−e−3φ1w1)(3e−2φ2w2−e−3φ2w2) |
=δ4(3e−2φ1w1−e−3φ1w1)(3e−2φ2w2−e−3φ2w2)(12(e−3φ1w1−3e−2φ1w1)+1)×(12(e−3φ2w2−3e−2φ2w2)+1)=δϕ(w1,w2), |
where
ϕ(w1,w2)=14(3e−2φ1w1−e−3φ1w1)(3e−2φ2w2−e−3φ2w2)(12(e−3φ1w1−3e−2φ1w1)+1)×(12(e−3φ2w2−3e−2φ2w2)+1)=F(w1)F(w2)R(w1)R(w2). |
For all values of w1 and w2, ϕ(w1,w2) is always non-negative because the DF and reliability function take values ranging from zero to one. On the other hand, we have for positive values of δ, δϕ(w1,w2)≥0 ∀w1,w2, that the EP-EX-FGM is PQD. Likewise, for negative values of δ, δϕ(w1,w2)≤0 ∀w1,w2, the EP-EX-FGM is NQD. Thus, EP-EX-FGM has both PQD and NQD.
Lad et al. [29] introduced the concept of extropy as dual to entropy, facilitating the comparison of uncertainties of two RVs. If the extropy of W1 is less than that of another variable W2, then W1 is said to have more uncertainty than W2. Due to the difficulty of deriving the entropy for model (1.6), our focus in this subsection will be on the extropy. Extropy has several applications, such as scoring the forecasting distributions using the total scoring rule, comparing the uncertainties of two RVs, etc. Recently, the literature has numerous research works on this measure, e.g. Abd Elgawad et al. [1], Husseiny and Syam [20], and Husseiny et al. [21].
It is possible to introduce a bivariate version of extropy based on the FGM family in the following theorem.
Theorem 4.2. Consider two non-negative continuous RVs W1 and W2 with JPDF fW1,W2(w1,w2). Then, the bivariate version of extropy based on FGM is given by
J(W1,W2)=(1+δ)2J(W1)J(W2)+4δ2ΨW1(2)ΨW2(2)+2δ(2δ+1)ΨW1(1)ΨW2(1)−4δ2(ΨW1(2)ΨW2(1)+ΨW1(1)ΨW2(2))+2δ(δ+1)(ΨW1(1)J(W2)++ΨW2(1)J(W1))−2δ2(ΨW1(2)J(W2)+ΨW2(2)J(W1)), | (4.5) |
where
J(Wi)=−12E(fWi(Wi))andΨWi(p)=E(fWi(Wi)FpWi(Wi)),i=1,2,p=1,2. | (4.6) |
Proof. By using (1.4), we get
J(W1,W2)=14E(fW1,W2(W1,W2))=14∫∞0∫∞0f2W1,W2(w1,w2)dw1dw2=14∫∞0∫∞0f2W1(w1)f2W2(w2)[1+δ2(2FW1(w1)−1)2(2FW2(w2)−1)2+2δ(2FW1(w1)−1)×(2FW2(w2)−1)]dw1dw2=(1+δ)24∫∞0∫∞0f2W1(w1)f2W2(w2)dw1dw2+4δ2∫∞0∫∞0f2W1(w1)f2W2(w2)F2W1(w1)F2W2(w2)dw1dw2+2δ(2δ+1)∫∞0∫∞0f2W1(w1)f2W2(w2)FW1(w1)FW2(w2)dw1dw2−4δ2[∫∞0∫∞0f2W1(w1)f2W2(w2)×(F2W1(w1)FW2(w2)+FW1(w1)F2W2(w2))dw1dw2]+δ2[∫∞0∫∞0f2W1(w1)f2W2(w2)(F2W1(w1)+F2W2(w2))dw1dw2]−δ(δ+1)[∫∞0∫∞0f2W1(w1)f2W2(w2)(FW1(w1)+FW2(w2))dw1dw2]. |
Since each bivariate integral in the above formula is separable into the product of its univariate integrals, and by incorporating (4.6) in the above integrations, the required result directly follows. This completes the proof.
Corolary 4.1. Let W1∼EP(φ1) and W2∼EP(φ2). Using (4.6), and after simple algebra, we get J(Wi)=−33φi80, Ψwi(1)=643φi2240, and Ψwi(2)=725φi4928,i=1,2. Thus, by using Theorem 4.2, the bivariate extropy based on EP-EX-FGM is
J(W1,W2)=9φ2(δ(5339912δ+9554281)φ127720+9317φ15)98560. |
In a spirit similar to that of the bivariate version of extropy, we can also introduce bivariate weighted extropy based on FGM family in the following theorem.
Theorem 4.3. Let W1 and W2 be non-negative continuous RVs with JPDF fW1,W2(w1,w2). Then, the bivariate weighted extropy based on FGM is given by
Jw(W1,W2)=(1+δ)2Jw(W1)Jw(W2)+4δ2Ψww1(2)ΨwW2(2)+2δ(2δ+1)ΨwW1(1)ΨwW2(1)−4δ2(ΨwW1(2)ΨwW2(1)+ΨwW1(1)ΨwW2(2))+2δ(δ+1)(ΨwW1(1)J(W2)+ΨwW2(1)J(W1))−2δ2(ΨwW1(2)Jw(W2)+ΨwW2(2)Jw(W1)), | (4.7) |
where
Jw(Wi)=−12E(WifWi(Wi)),andΨwWi(p)=E(WifWi(Wi)FpWi(Wi)),i=1,2,p=1,2. | (4.8) |
Proof. We obtain the results by applying Theorem 4.2's proving techniques.
Corolary 4.2. Let W1∼EP(φ1) and W2∼EP(φ2). Using (4.8), and after simple algebra, we get Jw(Wi)=−53400, ΨwWi(1)=8364975644800, and ΨwWi(2)=33010783341510400,i=1,2. Thus, by using Theorem 4.3, the bivariate weighted extropy based on EP-EX-FGM is
Jw(W1,W2)=101114226580249δ2384199200+949009137241δ6350400+13323648825303564800. |
Proposition 4.1. Let W1 and W2 follow an FGM family.
● If W2=aW1+b, then J(W2)=1aJ(W1).
● If δ=0, then J(W1,W2)=J(W1)J(W2).
It is worth noting that, as was shown by Balakrishnan et al. [7], there exist distributions with the same extropy, but different weighted extropy. Moreover, there exist distributions with the same weighted extropy, but different extropy.
Replacing the PDF in the extropy function with the survival function, Jahanshahi et al. [23] proposed a new measure of uncertainty of a non-negative continuous RV called cumulative residual extropy (CREX). Jahanshahi et al. [23] showed that if two RVs W1 and W2 are lifetimes of two systems A and B, and if the CREX of W1 is less than the CREX of W2, then system A has less uncertainty than system B. We can define the bivariate CREX based on FGM family in the following theorem.
Theorem 4.4. Let W1 and W1 be non-negative continuous RVs with JDF FW1,W2(w1,w2). Then, the bivariate CREX based on FGM is given by
Jc(W1,W2)=Jc(W1)Jc(W2)+δ24ΨcW1(2)ΨcW2(2)+δ2ΨcW1(1)ΨcW2(1), | (4.9) |
where
Jc(Wi)=−12∫∞0(¯FWi(wi))2dwiandΨcWi(p)=∫∞0(¯FWi(wi))2FpWi(wi)dwi,i=1,2,p=1,2. | (4.10) |
Proof. By using (1.3), we get
Jc(W1,W2)=14∫∞0∫∞0(¯FW1,W2(w1,w2))2dw1dw2=14∫∞0∫∞0(¯FW1(w1))2(¯FW2(w2))2[1+δ2F2W1(w1)F2W2(w2)+2δFW1(w1)FW2(w2)]dw1dw2=14∫∞0∫∞0(¯FW1(w1))2(¯FW2(w2))2dw1dw2+δ24∫∞0∫∞0F2W1(w1)F2W2(w2)(¯FW1(w1))2×(¯FW2(w2))2dw1dw2+δ2∫∞0∫∞0FW1(w1)FW2(w2)(¯FW1(w1))2(¯FW2(w2))2dw1dw2. |
Since each bivariate integral in the formula above can be divided into the product of its univariate integrals, the required result can be obtained immediately by incorporating (4.10) in the integrations above. The proof is now complete.
Corolary 4.3. Let W1∼EP(φ1) and W2∼EP(φ2). Using (4.10), and after simple algebra, we get Jc(Wi)=−73480φi, ΨcWi(1)=195720160φi, and ΨcWi(2)=20987443520φi,i=1,2. Thus, by using Theorem 4.4, the bivariate CREX based on EP-EX-FGM is
Jc(W1,W2)=δ(440454169δ+3707293832)+18199089216786839961600φ1φ2. |
Figure 3 displays the values of bivariate extropy, weighted extropy, and CREX in EP-EX-FGM. In 3D representation (a, b), we find that the value of J(W1,W2) increases as the values of φ1 and φ2 increase. Also, there is symmetry for the Jw(W1,W2) values in the 2D plot (c) according to δ values.
In this section, we discuss two estimation methods for estimating the unknown parameters of the EP-EX-FGM distribution: ML and Bayesian estimations. Moreover, we construct asymptotic confidence intervals by using the Fisher information matrix (FIM) for the model's parameters.
The ML technique is a widely used and significant statistical technique. By using the ML technique, one can obtain estimates of parameters that have favorable statistical properties, such as consistency, asymptotic unbiasedness, efficiency, and normality asymptotically. It is necessary to calculate the parameter estimates that maximize the probability of the sample data to obtain the parameter estimates with the ML method. The log-likelihood function lnL is obtained by using the PDF given in (2.2).
lnL=nln3φ12+nln3φ22+n∑i=1ln(2e−2φ1wi1−e−3φ1wi1)+n∑i=1ln(2e−2φ2wi2−e−3φ2wi2)+n∑i=1ln[1+δ(1−2(12(e−3φ1wi1−3e−2φ1wi1)+1))(1−2(12(e−3φ2wi2−3e−2φ2wi2)+1))]. |
We obtain the following normal equations by partially differentiating lnL with respect to the vector of parameters τ=(φ1,φ2,δ) and equating them to zero. The following are those derivatives:
∂lnL(τ)∂φl=n∑i=13δwile−3φlwil(3e−2φjwij−e−3φjwij−1)(1−2eφlwil)[1+δe−3(φlwil+φjwij)(1−3eφjwij+e3φjwij)(1−3eφlwil+e3φlwil)]+nφl+n∑i=1(3−4eφlwil)wil(2eφlwil−1), |
and
∂lnL(τ)∂δ=n∑i=1(3e−2φjwij−e−3φjwij−1)(3e−2φlwil−e−3φlwil−1)[1+δe−3(φlwil+φjwij)(1−3eφjwij+e3φjwij)(1−3eφlwil+e3φlwil)], |
where l,j=1,2, and l≠j.
Because the likelihood equations for φ1,φ2, and δ are nonlinear and challenging to solve analytically, numerical methods like the Newton-Raphson method can be employed to find the ML estimates of the distribution parameters φ1,φ2, and δ.
Based on the asymptotic normality of the ML estimates, the FIM is frequently used to create asymptotic confidence intervals for the unknown parameters in τ (for more details about other uses of FIM, see Barakat et al., [8]). Under specific assumptions of regularity, the ML estimates denoted as ˆτ are distributed according to the normal distribution. As the sample size n tends to infinity, the distribution of the estimator ˆτ=(^φ1,^φ2,ˆδ) approaches a normal distribution with mean τ and covariance matrix equal to the inverse of the FIM, denoted as I−1(τ). The FIM consists of the negative expected values of the second-order derivatives of lnL, as expressed by
I(τ)=−E[I^φ1^φ1I^φ2^φ1I^φ2^φ2Iˆδ^φ1Iˆδ^φ2Iˆδˆδ]. | (5.1) |
The entries of the matrix in (5.1) are given by
Iφlφl=∂2L(τ)∂φ2l=−nφ2l−n∑i=12w2ileφlwil(1−2eφlwil)2+n∑i=13δw2ileφlwil×[δ+δe2φlwil(4eφlwil−3)−3δeφjwij(1−3e2φlwil+4e3φlwil)+e3φjwij(δ+e2φlwil(4eφlwil−3)(1+δ))][δ+δeφlwil(e2φlwil−3)−3δeφjwij(1−3eφlwil+e3φlwil)+eφjwij(δ−3δeφlwil+(1+δ)e3φlwil)]2, |
Iφlφj=n∑i=1[9δwilwije3φlwile3φjwij(2eφlwil−1)(eφjwij−1)][δ+δeφlwil(e2φlwil−3)−3δeφjwij(1−3eφlwil+e3φlwil)+eφjwij(δ−3δe3φlwil+(1+δ)e3φlwil)]2, |
Iφlδ=n∑i=1[3e3wilφl+3wijφj(2ewilφl)(1−3ewijφj+e3wijφj)][δ+δeφlwil(e2φlwil−3)−3δeφjwij(1−3eφlwil+e3φlwil)+eφjwij(δ−3δeφlwil+(1+δ)e3φlwil)]2, |
Iδδ=−n∑i=1(1−3eφlwil+e3φlwil)2(1−3eφjwij+e3φjwij)2[δ+δeφlwil(e2φlwil−3)−3δeφjwij(1−3eφlwil+e3φlwil)+eφjwij(δ−3δe3φlwil+(1+δ)e3φlwil)]2, |
where l,j=1,2,l≠j. According to Jia et al. [26], in cases where it is challenging to obtain the anticipated values directly, an alternative approach is to estimate them by calculating the negative second-order derivatives of the natural logarithm of the likelihood function, evaluated at the ML estimates. Hence, we approximate the expected values by the negatives of the second-order derivatives evaluated at the ML estimates ˆτ. Thus, the estimated value of the FIM is I(ˆτ). Moreover, 100(1−θ) asymptotic confidence intervals for the parameter τ can be constructed as follows:
^φl±Zθ2√Jˆφlˆφl;ˆδ±Zθ2√Jˆδˆδ,l=1,2, |
where Jqlql,ql=ˆφl,ˆδ, is the element in I−1(ˆτ), corresponding to the element Iqlql in the estimated matrix I(ˆτ), and Zθ2 is the percentile of the standard normal distribution with right tail probability θ2.
The Bayesian estimation technique is a powerful tool for estimating unknown parameters based on observable data. Using Bayes, theorem, a concept in probability theory, new information can be gathered to update the probability of a hypothesis. Because this approach takes prior knowledge into account when estimating, it provides some advantages over the traditional ML approach. Moreover, it is capable of assessing the degree of uncertainty surrounding each parameter. We must select an acceptable prior PDF and hyper-parameter values that reflect our belief regarding the data. While employing a Bayesian technique, the data analyst needs to be more explicit about all modeling assumptions. Choosing a suitable prior distribution usually involves taking into consideration the distribution's basic form (shape and domain) as well as its key characteristics (variance and mean). For the parameters φl, l=1,2, we select gamma-independent priors, specifically
Π(φl)∝φal−1le−vlφl,φl>0,al,vl>0,l=1,2, |
while the copula parameter δ has uniform prior distribution defined on (−1,1). The prior JPDF is given by
Π(τ)∝φal−1le−vlφl. |
The likelihood method's estimate and variance-covariance matrix can be used to determine how to elicit the independent joint prior's hyper-parameters. The gamma prior's mean and variance can be used to represent the derived hyper-parameters. For more information, see Gupta and Kundu [18], Dey et al. [13], and Hamdy and Almetwally [19]. The parameters φl, l=1,2, of EP-EX-FGM should be well-known and positive. The likelihood function is given by
L(τ)=(3φ12)nn∏i=1(2e−2φ1wi1−e−3φ1wi1)(3φ22)nn∏i=1(2e−2φ2wi2−e−3φ2wi2)×n∏i=1[1+δ(1−2(12(e−3φ1wi1−3e−2φ1wi1)+1))(1−2(12(e−3φ2wi2−3e−2φ2wi2)+1))]. |
The corresponding posterior density is given as follows:
Π(τ|w1,w2)∝(32)nφn+a1−11e−v1φ1n∏i=1(2e−2φ1wi1−e−3φ1wi1)(32)nφn+a2−12e−v2φ2n∏i=1(2e−2φ2wi2−e−3φ2wi2)×n∏i=1[1+δ(1−2(12(e−3φ1wi1−3e−2φ1wi1)+1))(1−2(12(e−3φ2wi2−3e−2φ2wi2)+1))]. |
The marginal posterior distributions Π(φl|δ,w1,w2), and Π(δ|φl,w1,w2) of the parameter φl,l=1,2, may be found by integrating out the nuisance parameters from the posterior distribution Π(τ|w1,w2) as follows:
Π(φl|w1,w2)∝φn+al−1le−vlφln∏i=1(2e−2φlwil−e−3φlwil)n∏i=1[1+δ(1−2(12(e−3φ1wi1−3e−2φ1wi1)+1))×(1−2(12(e−3φ2wi2−3e−2φ2wi2)+1))], |
and
Π(δ|w1,w2)∝n∏i=1[1+δ(1−2(12(e−3φ1wi1−3e−2φ1wi1)+1))(1−2(12(e−3φ2wi2−3e−2φ2wi2)+1))], |
where l=1,2. We use the well-known squared error loss function, which yields the posterior means as the Bayes estimates of τ, say (^φl,ˆδ), which are given by ^φl=∫∞0φlΠ(φl|w1,w2)dφl and ˆδ=∫1−1δΠ(δ|w1,w2)dδ. Notice that the preceding integrals cannot be obtained explicitly. Because of this, we use the Monte Carlo method to find an approximate value of these integrals, see Tables 1–4.
φ1=0.6, φ2=3 | |||||||||
δ | −0.4 | 0.5 | |||||||
ML estimate | Bayesian estimate | ML estimate | Bayesian estimate | ||||||
n | Bias | MSE | Bias | MSE | Bias | MSE | Bias | MSE | |
20 | φ1 | 0.045 | 0.257 | 0.032 | 0.245 | 0.087 | 0.357 | 0.054 | 0.321 |
φ2 | 0.098 | 0.784 | 0.085 | 0.699 | 0.110 | 0.821 | 0.099 | 0.711 | |
δ | 0.074 | 0.457 | 0.071 | 0.416 | 0.085 | 0.524 | 0.078 | 0.511 | |
50 | φ1 | 0.038 | 0.241 | 0.029 | 0.233 | 0.078 | 0.328 | 0.042 | 0.299 |
φ2 | 0.082 | 0.698 | 0.072 | 0.587 | 0.096 | 0.745 | 0.082 | 0.621 | |
δ | 0.071 | 0.421 | 0.065 | 0.387 | 0.068 | 0.389 | 0.052 | 0.354 | |
100 | φ1 | 0.029 | 0.239 | 0.022 | 0.198 | 0.062 | 0.289 | 0.034 | 0.247 |
φ2 | 0.075 | 0.631 | 0.064 | 0.514 | 0.082 | 0.687 | 0.071 | 0.587 | |
δ | 0.067 | 0.401 | 0.059 | 0.345 | 0.056 | 0.341 | 0.048 | 0.312 | |
150 | φ1 | 0.015 | 0.214 | 0.014 | 0.167 | 0.058 | 0.245 | 0.028 | 0.187 |
φ2 | 0.064 | 0.543 | 0.052 | 0.489 | 0.075 | 0.578 | 0.068 | 0.517 | |
δ | 0.058 | 0.389 | 0.041 | 0.312 | 0.051 | 0.287 | 0.038 | 0.287 |
φ1=2, φ2=0.3 | |||||||||
δ | −0.4 | 0.5 | |||||||
ML estimate | Bayesian estimate | ML estimate | Bayesian estimate | ||||||
n | Bias | MSE | Bias | MSE | Bias | MSE | Bias | MSE | |
20 | φ1 | 0.075 | 0.547 | 0.065 | 0.511 | 0.095 | 0.655 | 0.078 | 0.625 |
φ2 | 0.109 | 0.324 | 0.099 | 0.301 | 0.125 | 0.387 | 0.134 | 0.336 | |
δ | 0.041 | 0.245 | 0.032 | 0.211 | 0.058 | 0.389 | 0.049 | 0.380 | |
50 | φ1 | 0.069 | 0.537 | 0.056 | 0.489 | 0.089 | 0.587 | 0.065 | 0.587 |
φ2 | 0.098 | 0.287 | 0.087 | 0.287 | 0.117 | 0.354 | 0.128 | 0.312 | |
δ | 0.032 | 0.231 | 0.028 | 0.201 | 0.047 | 0.345 | 0.041 | 0.311 | |
100 | φ1 | 0.058 | 0.510 | 0.048 | 0.452 | 0.074 | 0.543 | 0.052 | 0.521 |
φ2 | 0.082 | 0.241 | 0.075 | 0.265 | 0.099 | 0.299 | 0.114 | 0.278 | |
δ | 0.025 | 0.201 | 0.018 | 0.241 | 0.041 | 0.311 | 0.038 | 0.289 | |
150 | φ1 | 0.042 | 0.465 | 0.035 | 0.421 | 0.062 | 0.487 | 0.041 | 0.487 |
φ2 | 0.075 | 0.211 | 0.069 | 0.214 | 0.078 | 0.254 | 0.102 | 0.263 | |
δ | 0.021 | 0.187 | 0.012 | 0.411 | 0.032 | 0.266 | 0.024 | 0.211 |
φ1=0.5 φ2=0.7 | |||||||||
δ | −0.4 | 0.5 | |||||||
ML estimate | Bayesian estimate | ML estimate | Bayesian estimate | ||||||
n | Bias | MSE | Bias | MSE | Bias | MSE | Bias | MSE | |
20 | φ1 | 0.057 | 0.258 | 0.049 | 0.245 | 0.078 | 0.289 | 0.069 | 0.257 |
φ2 | 0.069 | 0.298 | 0.054 | 0.285 | 0.089 | 0.314 | 0.075 | 0.301 | |
δ | 0.111 | 0.435 | 0.095 | 0.411 | 0.121 | 0.542 | 0.102 | 0.521 | |
50 | φ1 | 0.048 | 0.246 | 0.035 | 0.234 | 0.069 | 0.258 | 0.053 | 0.241 |
φ2 | 0.055 | 0.278 | 0.042 | 0.262 | 0.065 | 0.295 | 0.069 | 0.278 | |
δ | 0.096 | 0.421 | 0.091 | 0.387 | 0.112 | 0.487 | 0.087 | 0.410 | |
100 | φ1 | 0.035 | 0.232 | 0.029 | 0.221 | 0.052 | 0.241 | 0.045 | 0.225 |
φ2 | 0.042 | 0.265 | 0.034 | 0.251 | 0.052 | 0.274 | 0.061 | 0.197 | |
δ | 0.085 | 0.411 | 0.081 | 0.398 | 0.089 | 0.478 | 0.085 | 0.456 | |
150 | φ1 | 0.028 | 0.211 | 0.021 | 0.202 | 0.043 | 0.234 | 0.037 | 0.215 |
φ2 | 0.031 | 0.245 | 0.021 | 0.243 | 0.035 | 0.251 | 0.052 | 0.165 | |
δ | 0.079 | 0.398 | 0.079 | 0.378 | 0.081 | 0.415 | 0.074 | 0.741 |
φ1=2 φ2=4 | |||||||||
δ | −0.4 | 0.5 | |||||||
ML estimate | Bayesian estimate | ML estimate | Bayesian estimate | ||||||
n | Bias | MSE | Bias | MSE | Bias | MSE | Bias | MSE | |
20 | φ1 | 0.086 | 0.457 | 0.075 | 0.421 | 0.092 | 0.511 | 0.084 | 0.498 |
φ2 | 0.098 | 0.578 | 0.085 | 0.543 | 0.099 | 0.654 | 0.095 | 0.625 | |
δ | 0.078 | 0.254 | 0.069 | 0.247 | 0.089 | 0.347 | 0.081 | 0.321 | |
50 | φ1 | 0.078 | 0.423 | 0.068 | 0.387 | 0.085 | 0.482 | 0.071 | 0.453 |
φ2 | 0.085 | 0.562 | 0.078 | 0.523 | 0.087 | 0.589 | 0.081 | 0.601 | |
δ | 0.069 | 0.242 | 0.061 | 0.241 | 0.081 | 0.341 | 0.074 | 0.298 | |
100 | φ1 | 0.065 | 0.387 | 0.053 | 0.354 | 0.075 | 0.421 | 0.053 | 0.421 |
φ2 | 0.074 | 0.523 | 0.062 | 0.478 | 0.072 | 0.512 | 0.063 | 0.587 | |
δ | 0.062 | 0.214 | 0.059 | 0.201 | 0.078 | 0.287 | 0.071 | 0.274 | |
150 | φ1 | 0.058 | 0.345 | 0.042 | 0.299 | 0.068 | 0.388 | 0.046 | 0.401 |
φ2 | 0.062 | 0.487 | 0.058 | 0.463 | 0.066 | 0.483 | 0.058 | 0.536 | |
δ | 0.054 | 0.203 | 0.041 | 0.189 | 0.069 | 0.251 | 0.061 | 0.232 |
The performance of ML and Bayesian estimates are contrasted numerically in this section. For the parameters of the EP-EX-FGM model, the performance of the various techniques and the analytically deduced results may be evaluated exactly. The Mathcad package was used, and 1000 samples from an EP-EX-FGM model had been gathered. The values of the parameters can be defined as follows:
● In Table 1: φ1=0.6, φ2=3.
● In Table 2: φ1=2, φ2=0.3.
● In Table 3: φ1=0.5, φ2=0.7.
● In Table 4: φ1=2, φ2=4.
The sample-sizes n are 20, 50,100, and 150. The simulation results of bias and mean squared error (MSE) on 5000 iterations of Monte Carlo simulation are shown in Tables 1–4. The following conclusions can be drawn from Tables 1–4:
● The ML and Bayesian estimates of unknown parameters are fairly good in terms of bias and MSE.
● With an increase in sample size, the MSEs decrease and the estimated values of the parameters approach the nominal values of the parameters.
● For δ=−0.4, both ML and Bayesian estimate values are smaller than the case of δ=0.5.
Moreover, Figure 4 shows the comparison between ML and Bayesian estimations for both φ1 and φ2 at specific values of the parameters. This figure shows that the two techniques give close values.
In this section, we use the EP-EX-FGM model to examine two bivariate real data sets. The benefits of EP-EX-FGM over alternative bivariate models will be thoroughly discussed in the first one, which focuses on computer science. Additionally, several arguments supporting the fit of the estimated theoretical model to the empirical data will be presented. We talk briefly about the second application, which relates to medical applications.
Based on Oliveira et al. [34], data was gathered and analyzed. The data set contains n=50 simulated rudimentary computer systems with processors (W1) and memory (W2). An operating computer will be able to operate when both parts are working properly (the processors and memory). Assume the system is nearing the end of its lifecycle. The degeneration advances rapidly in a short period. In the case of the first component, a deadly shock can destroy either it or the second component at random, due to the system's greater vulnerability to shocks (see, Ahmad et al. [3]). The independence presumption is not accurate because both components can be killed by a deadly shock at the same time, so we discuss this issue using the FGM copula. We fit the EP-EX distribution to the processor lifetime and memory lifetime separately. The ML estimates of the scale and shape parameters are ^φ1=0.345952, ^φ2=0.362599, and ˆδ=0.452819. To analyze the maximum values of the estimators by profile likelihood, Figures 5 and 6 were constructed based on W1 and W2. Also, Figures 7–12 illustrate statistical visualizations, including a scatter plot, empirical CDF, Q-Q plot, a density plot, paired smooth histogram, and a paired violin plot. Table 5 clearly shows that, in terms of data fit, the EP-EX-FGM model performs better than the bivariate generalized EX-FGM distribution (GEX-FGM), the bivariate exponential FGM distribution (EX-FGM), and the bivariate Weibull FGM distribution (W-FGM). Furthermore, by using the Python programming language, our data set, and the Kolmogorov-Smirnov (KS) test, the EP-EX-FGM model demonstrates an outstanding match with a p-value of 0.832 and a test statistic of 0.085.
−lnL | AIC | AICc | BIC | HQIC | CAIC | |
EP-EX-FGM | 150.679 | 307.358 | 307.88 | 313.095 | 309.543 | 307.88 |
GEX-FGM | 148.861 | 307.722 | 309.086 | 317.282 | 311.363 | 309.086 |
EX-FGM | 151.412 | 308.823 | 309.345 | 314.559 | 311.007 | 309.345 |
W-FGM | 149.915 | 309.829 | 311.193 | 319.389 | 313.47 | 311.193 |
We now use the EP-EX-FGM family to fit a second real data set that Grover et al. [16] covered. We conducted our investigation using two data sets. Retrospective data on 132 individuals diagnosed with type 2 diabetes according to ADA guidelines is included in the first dataset, which originates from Dr. Lal's Path Lab, a respected NABL-certified path lab. To collect up-to-date pathology reports for these patients, researchers conducted a house-to-house survey that began when they were diagnosed with diabetes and ended in November 2007. The duration of diabetes, fasting blood glucose, diastolic blood pressure, systolic blood pressure, low-density lipoprotein, and serum creatinine levels were all documented for each patient, starting from childhood and continuing into adulthood. The mean serum creatinine level (SrCr) and the mean duration of diabetes are represented by the RVs W1 and W2, respectively.
This study only looks at kidney problems caused by type 2 diabetes, therefore it automatically ignores the effects on the eyes, heart, and other organs. Additionally, we have removed cases where renal problems occurred before diabetes. Participants in our research with individuals with the same duration of diabetes experience variable degrees of kidney function. A patient's renal health is evaluated using SrCr because its fast rise in value is a critical sign for DN risk prediction. So, the data were split into two groups based on SrCr values: DN (SrCr 1.4 mg/dL) and NDN (Non-Diabetic Nephropathy; SrCr 1.4 mg/dL). At the end of the trial, 60 (or 45.45%) of the 132 individuals had DN, and 72 (or 54.55% of the total) had NDN. The ML estimates of the scale and shape parameters are ^φ1=0.423,^φ2=0.0433, and ˆδ=0.98. Moreover, we estimate AIC = 202.373 and BIC = 205.206.
This study introduces a new bivariate EP-EX-FGM model: an improved version of the bivariate distribution based on the FGM copula. In the area of bivariate modeling, the proposed distribution represents a substantial and original contribution. This study introduces a new statistical model called EP-EX-FGM, which is a bivariate EP-EX distribution based on the FGM copula. Within the analysis of bivariate data, this work is both important and innovative in the field of bivariate modeling. The correlation coefficient between W1 and W2 for the suggested distribution EP-EX-FGM ranges from a maximum value of 0.261 to a minimum value of −0.261. In addition, we have explored dependability measures such as the hazard function, the MRL function, and the vitality function. Moreover, it has been demonstrated that the proposed model satisfies the PQD(NQD) characteristic, depending on the sign of the shape. The parameter estimators were generated using ML and Bayesian approaches, and the overall outcome indicates that Bayesian estimation outperforms its counterpart. Furthermore, a Monte Carlo simulation study was conducted to assess the performance of the estimators. Asymptotic confidence intervals for the likelihood estimation were produced for the parameters in this model. Finally, the significance and adaptability of the EP-EX-FGM were investigated through the analysis of real data sets.
H. M. Barakat: Conceptualization, formal analysis, writing original draft, software, investigation, methodology, supervision; M. A. Alawady: Conceptualization, formal analysis, writing original draft, software, investigation, methodology, supervision; I. A. Husseiny: Conceptualization, writing original draft, formal analysis, software, investigation, methodology, supervision; M. Nagy: Validation, resources, writing – review & editing, data curation, methodology; A. H. Mansi: Writing – review & editing, investigation; M. O. Mohamed: Conceptualization, formal analysis, writing original draft, software, investigation, methodology, supervision; All authors have read and agreed to the published version of the manuscript.
The authors are grateful to the editor and anonymous referees for their insightful comments and suggestions, which helped to improve the paper's presentation. This research was conducted under a project titled "Researchers Supporting Project", funded by King Saud University, Riyadh, Saudi Arabia under grant number (RSPD2024R969).
This research was conducted under a project titled "Researchers Supporting Project", funded by King Saud University, Riyadh, Saudi Arabia under grant number (RSPD2024R969).
The authors declare no conflicts of interest.
[1] |
Beard BC, Wilson SH, Smerdon MJ (2003) Suppressed catalytic activity of base excision repair enzymes on rotationally positioned uracil in nucleosomes. Proc Natl Acad Sci U S A 100: 7465-7470. doi: 10.1073/pnas.1330328100
![]() |
[2] |
Caldecott KW (2007) Mammalian single-strand break repair: mechanisms and links with chromatin. DNA Repair (Amst) 6: 443-453. doi: 10.1016/j.dnarep.2006.10.006
![]() |
[3] |
De Vos M, Schreiber V, Dantzer F (2012) The diverse roles and clinical relevance of PARPs in DNA damage repair: current state of the art. Biochem Pharmacol 84: 137-146. doi: 10.1016/j.bcp.2012.03.018
![]() |
[4] |
Kraus WL (2008) Transcriptional control by PARP-1: chromatin modulation, enhancer-binding, coregulation, and insulation. Curr Opin Cell Biol 20: 294-302. doi: 10.1016/j.ceb.2008.03.006
![]() |
[5] |
Ali AA, Timinszky G, Arribas-Bosacoma R, et al. (2012) The zinc-finger domains of PARP1 cooperate to recognize DNA strand breaks. Nat Struct Mol Biol 19: 685-692. doi: 10.1038/nsmb.2335
![]() |
[6] |
Eustermann S, Wu WF, Langelier MF, et al. (2015) Structural Basis of Detection and Signaling of DNA Single-Strand Breaks by Human PARP-1. Mol Cell 60: 742-754. doi: 10.1016/j.molcel.2015.10.032
![]() |
[7] |
Langelier MF, Planck JL, Roy S, et al. (2012) Structural basis for DNA damage-dependent poly(ADP-ribosyl)ation by human PARP-1. Science 336: 728-732. doi: 10.1126/science.1216338
![]() |
[8] |
Martinez-Zamudio R, Ha HC (2012) Histone ADP-ribosylation facilitates gene transcription by directly remodeling nucleosomes. Mol Cell Biol 32: 2490-2502. doi: 10.1128/MCB.06667-11
![]() |
[9] |
Tanuma S, Kawashima K, Endo H (1985) Comparison of ADP-ribosylation of chromosomal proteins between intact and broken cells. Biochem Biophys Res Commun 127: 896-902. doi: 10.1016/S0006-291X(85)80028-0
![]() |
[10] |
Gunderson CC, Moore KN (2015) Olaparib: an oral PARP-1 and PARP-2 inhibitor with promising activity in ovarian cancer. Future Oncol 11: 747-757. doi: 10.2217/fon.14.313
![]() |
[11] |
Fong PC, Boss DS, Yap TA, et al. (2009) Inhibition of poly(ADP-ribose) polymerase in tumors from BRCA mutation carriers. N Engl J Med 361: 123-134. doi: 10.1056/NEJMoa0900212
![]() |
[12] |
Clark NJ, Kramer M, Muthurajan UM, et al. (2012) Alternative modes of binding of poly(ADP-ribose) polymerase 1 to free DNA and nucleosomes. J Biol Chem 287: 32430-32439. doi: 10.1074/jbc.M112.397067
![]() |
[13] |
Kudryashova KS, Chertkov OV, Nikitin DV, et al. (2015) Preparation of Mononucleosomal Templates for Analysis of Transcription with RNA Polymerase Using spFRET. Methods Mol Biol 1288: 395-412. doi: 10.1007/978-1-4939-2474-5_23
![]() |
[14] | Chertkov OV, Studitsky VM, Feofanov AV, et al. (2016) Change in conformation of linker DNA upon binding of histone Н1.5 to nucleosome: fluorescent microscopy of single complexes. Mosc Univ Biol Sci Bull 71: 108-113. |
[15] |
Feofanov AV, Kudryashova KS, Chertkov OV, et al. (2015) Analysis of nucleosome transcription using single-particle FRET. Springer Proc Phys 164: 255-260. doi: 10.1007/978-3-319-16919-4_33
![]() |
[16] | Valieva ME, Armeev GA, Kudryashova KS, et al. (2016) Large-Scale ATP-Independent Nucleosome Unfolding by a Histone Chaperone. Nat Struct Mol Biol: In press. |
[17] |
Langelier MF, Planck JL, Servent KM, et al. (2011) Purification of human PARP-1 and PARP-1 domains from Escherichia coli for structural and biochemical analysis. Methods Mol Biol 780: 209-226. doi: 10.1007/978-1-61779-270-0_13
![]() |
[18] |
Kulaeva OI, Gaykalova DA, Pestov NA, et al. (2009) Mechanism of chromatin remodeling and recovery during passage of RNA polymerase II. Nat Struct Mol Biol 16: 1272-1278. doi: 10.1038/nsmb.1689
![]() |
[19] |
Kireeva ML, Walter W, Tchernajenko V, et al. (2002) Nucleosome remodeling induced by RNA polymerase II: loss of the H2A/H2B dimer during transcription. Mol Cell 9: 541-552. doi: 10.1016/S1097-2765(02)00472-0
![]() |
[20] |
Studitsky VM, Clark DJ, Felsenfeld G (1995) Overcoming a nucleosomal barrier to transcription. Cell 83: 19-27. doi: 10.1016/0092-8674(95)90230-9
![]() |
[21] |
Vasudevan D, Chua EY, Davey CA (2010) Crystal structures of nucleosome core particles containing the '601' strong positioning sequence. J Mol Biol 403: 1-10. doi: 10.1016/j.jmb.2010.08.039
![]() |
[22] |
Thastrom A, Lowary PT, Widlund HR, et al. (1999) Sequence motifs and free energies of selected natural and non-natural nucleosome positioning DNA sequences. J Mol Biol 288: 213-229. doi: 10.1006/jmbi.1999.2686
![]() |
[23] |
Muthurajan UM, Hepler MR, Hieb AR, et al. (2014) Automodification switches PARP-1 function from chromatin architectural protein to histone chaperone. Proc Natl Acad Sci U S A 111: 12752-12757. doi: 10.1073/pnas.1405005111
![]() |
[24] | Zahradka P, Ebisuzaki K (1982) A shuttle mechanism for DNA-protein interactions. The regulation of poly(ADP-ribose) polymerase. Eur J Biochem 127: 579-585. |
[25] | Ogata N, Ueda K, Kawaichi M, et al. (1981) Poly(ADP-ribose) synthetase, a main acceptor of poly(ADP-ribose) in isolated nuclei. J Biol Chem 256: 4135-4137. |
[26] | Steffen JD, McCauley MM, Pascal JM (2016) Fluorescent sensors of PARP-1 structural dynamics and allosteric regulation in response to DNA damage. Nucleic Acids Res: gkw710. |
[27] |
Dawicki-McKenna JM, Langelier MF, DeNizio JE, et al. (2015) PARP-1 Activation Requires Local Unfolding of an Autoinhibitory Domain. Mol Cell 60: 755-768. doi: 10.1016/j.molcel.2015.10.013
![]() |
[28] |
Lee JY, Lee TH (2012) Effects of DNA methylation on the structure of nucleosomes. J Am Chem Soc 134: 173-175. doi: 10.1021/ja210273w
![]() |
[29] |
Lee JY, Wei S, Lee TH (2011) Effects of histone acetylation by Piccolo NuA4 on the structure of a nucleosome and the interactions between two nucleosomes. J Biol Chem 286: 11099-11109. doi: 10.1074/jbc.M110.192047
![]() |
[30] | Chang HW, Shaytan AK, Hsieh FK, et al. (2013) Structural Analysis of the Key Intermediate Formed during Transcription through a Nucleosome. Trends Cell Mol Biol 8: 13-23. |
[31] |
Pestov NA, Gerasimova NS, Kulaeva OI, et al. (2015) Structure of transcribed chromatin is a sensor of DNA damage. Sci Adv 1: e1500021. doi: 10.1126/sciadv.1500021
![]() |
[32] |
Gaykalova DA, Kulaeva OI, Volokh O, et al. (2015) Structural analysis of nucleosomal barrier to transcription. Proc Natl Acad Sci U S A 112: E5787-5795. doi: 10.1073/pnas.1508371112
![]() |
[33] |
Zaret KS, Carroll JS (2011) Pioneer transcription factors: establishing competence for gene expression. Genes Dev 25: 2227-2241. doi: 10.1101/gad.176826.111
![]() |
[34] |
Bohm V, Hieb AR, Andrews AJ, et al. (2011) Nucleosome accessibility governed by the dimer/tetramer interface. Nucleic Acids Res 39: 3093-3102. doi: 10.1093/nar/gkq1279
![]() |
[35] |
Tomschik M, Zheng H, van Holde K, et al. (2005) Fast, long-range, reversible conformational fluctuations in nucleosomes revealed by single-pair fluorescence resonance energy transfer. Proc Natl Acad Sci U S A 102: 3278-3283. doi: 10.1073/pnas.0500189102
![]() |
[36] |
Zlatanova J, Bishop TC, Victor JM, et al. (2009) The nucleosome family: dynamic and growing. Structure 17: 160-171. doi: 10.1016/j.str.2008.12.016
![]() |
[37] | Xu Y, Ayrapetov MK, Xu C, et al. (2012) Histone H2A.Z controls a critical chromatin remodeling step required for DNA double-strand break repair. Mol Cell 48: 723-733. |
φ1=0.6, φ2=3 | |||||||||
δ | −0.4 | 0.5 | |||||||
ML estimate | Bayesian estimate | ML estimate | Bayesian estimate | ||||||
n | Bias | MSE | Bias | MSE | Bias | MSE | Bias | MSE | |
20 | φ1 | 0.045 | 0.257 | 0.032 | 0.245 | 0.087 | 0.357 | 0.054 | 0.321 |
φ2 | 0.098 | 0.784 | 0.085 | 0.699 | 0.110 | 0.821 | 0.099 | 0.711 | |
δ | 0.074 | 0.457 | 0.071 | 0.416 | 0.085 | 0.524 | 0.078 | 0.511 | |
50 | φ1 | 0.038 | 0.241 | 0.029 | 0.233 | 0.078 | 0.328 | 0.042 | 0.299 |
φ2 | 0.082 | 0.698 | 0.072 | 0.587 | 0.096 | 0.745 | 0.082 | 0.621 | |
δ | 0.071 | 0.421 | 0.065 | 0.387 | 0.068 | 0.389 | 0.052 | 0.354 | |
100 | φ1 | 0.029 | 0.239 | 0.022 | 0.198 | 0.062 | 0.289 | 0.034 | 0.247 |
φ2 | 0.075 | 0.631 | 0.064 | 0.514 | 0.082 | 0.687 | 0.071 | 0.587 | |
δ | 0.067 | 0.401 | 0.059 | 0.345 | 0.056 | 0.341 | 0.048 | 0.312 | |
150 | φ1 | 0.015 | 0.214 | 0.014 | 0.167 | 0.058 | 0.245 | 0.028 | 0.187 |
φ2 | 0.064 | 0.543 | 0.052 | 0.489 | 0.075 | 0.578 | 0.068 | 0.517 | |
δ | 0.058 | 0.389 | 0.041 | 0.312 | 0.051 | 0.287 | 0.038 | 0.287 |
φ1=2, φ2=0.3 | |||||||||
δ | −0.4 | 0.5 | |||||||
ML estimate | Bayesian estimate | ML estimate | Bayesian estimate | ||||||
n | Bias | MSE | Bias | MSE | Bias | MSE | Bias | MSE | |
20 | φ1 | 0.075 | 0.547 | 0.065 | 0.511 | 0.095 | 0.655 | 0.078 | 0.625 |
φ2 | 0.109 | 0.324 | 0.099 | 0.301 | 0.125 | 0.387 | 0.134 | 0.336 | |
δ | 0.041 | 0.245 | 0.032 | 0.211 | 0.058 | 0.389 | 0.049 | 0.380 | |
50 | φ1 | 0.069 | 0.537 | 0.056 | 0.489 | 0.089 | 0.587 | 0.065 | 0.587 |
φ2 | 0.098 | 0.287 | 0.087 | 0.287 | 0.117 | 0.354 | 0.128 | 0.312 | |
δ | 0.032 | 0.231 | 0.028 | 0.201 | 0.047 | 0.345 | 0.041 | 0.311 | |
100 | φ1 | 0.058 | 0.510 | 0.048 | 0.452 | 0.074 | 0.543 | 0.052 | 0.521 |
φ2 | 0.082 | 0.241 | 0.075 | 0.265 | 0.099 | 0.299 | 0.114 | 0.278 | |
δ | 0.025 | 0.201 | 0.018 | 0.241 | 0.041 | 0.311 | 0.038 | 0.289 | |
150 | φ1 | 0.042 | 0.465 | 0.035 | 0.421 | 0.062 | 0.487 | 0.041 | 0.487 |
φ2 | 0.075 | 0.211 | 0.069 | 0.214 | 0.078 | 0.254 | 0.102 | 0.263 | |
δ | 0.021 | 0.187 | 0.012 | 0.411 | 0.032 | 0.266 | 0.024 | 0.211 |
φ1=0.5 φ2=0.7 | |||||||||
δ | −0.4 | 0.5 | |||||||
ML estimate | Bayesian estimate | ML estimate | Bayesian estimate | ||||||
n | Bias | MSE | Bias | MSE | Bias | MSE | Bias | MSE | |
20 | φ1 | 0.057 | 0.258 | 0.049 | 0.245 | 0.078 | 0.289 | 0.069 | 0.257 |
φ2 | 0.069 | 0.298 | 0.054 | 0.285 | 0.089 | 0.314 | 0.075 | 0.301 | |
δ | 0.111 | 0.435 | 0.095 | 0.411 | 0.121 | 0.542 | 0.102 | 0.521 | |
50 | φ1 | 0.048 | 0.246 | 0.035 | 0.234 | 0.069 | 0.258 | 0.053 | 0.241 |
φ2 | 0.055 | 0.278 | 0.042 | 0.262 | 0.065 | 0.295 | 0.069 | 0.278 | |
δ | 0.096 | 0.421 | 0.091 | 0.387 | 0.112 | 0.487 | 0.087 | 0.410 | |
100 | φ1 | 0.035 | 0.232 | 0.029 | 0.221 | 0.052 | 0.241 | 0.045 | 0.225 |
φ2 | 0.042 | 0.265 | 0.034 | 0.251 | 0.052 | 0.274 | 0.061 | 0.197 | |
δ | 0.085 | 0.411 | 0.081 | 0.398 | 0.089 | 0.478 | 0.085 | 0.456 | |
150 | φ1 | 0.028 | 0.211 | 0.021 | 0.202 | 0.043 | 0.234 | 0.037 | 0.215 |
φ2 | 0.031 | 0.245 | 0.021 | 0.243 | 0.035 | 0.251 | 0.052 | 0.165 | |
δ | 0.079 | 0.398 | 0.079 | 0.378 | 0.081 | 0.415 | 0.074 | 0.741 |
φ1=2 φ2=4 | |||||||||
δ | −0.4 | 0.5 | |||||||
ML estimate | Bayesian estimate | ML estimate | Bayesian estimate | ||||||
n | Bias | MSE | Bias | MSE | Bias | MSE | Bias | MSE | |
20 | φ1 | 0.086 | 0.457 | 0.075 | 0.421 | 0.092 | 0.511 | 0.084 | 0.498 |
φ2 | 0.098 | 0.578 | 0.085 | 0.543 | 0.099 | 0.654 | 0.095 | 0.625 | |
δ | 0.078 | 0.254 | 0.069 | 0.247 | 0.089 | 0.347 | 0.081 | 0.321 | |
50 | φ1 | 0.078 | 0.423 | 0.068 | 0.387 | 0.085 | 0.482 | 0.071 | 0.453 |
φ2 | 0.085 | 0.562 | 0.078 | 0.523 | 0.087 | 0.589 | 0.081 | 0.601 | |
δ | 0.069 | 0.242 | 0.061 | 0.241 | 0.081 | 0.341 | 0.074 | 0.298 | |
100 | φ1 | 0.065 | 0.387 | 0.053 | 0.354 | 0.075 | 0.421 | 0.053 | 0.421 |
φ2 | 0.074 | 0.523 | 0.062 | 0.478 | 0.072 | 0.512 | 0.063 | 0.587 | |
δ | 0.062 | 0.214 | 0.059 | 0.201 | 0.078 | 0.287 | 0.071 | 0.274 | |
150 | φ1 | 0.058 | 0.345 | 0.042 | 0.299 | 0.068 | 0.388 | 0.046 | 0.401 |
φ2 | 0.062 | 0.487 | 0.058 | 0.463 | 0.066 | 0.483 | 0.058 | 0.536 | |
δ | 0.054 | 0.203 | 0.041 | 0.189 | 0.069 | 0.251 | 0.061 | 0.232 |
−lnL | AIC | AICc | BIC | HQIC | CAIC | |
EP-EX-FGM | 150.679 | 307.358 | 307.88 | 313.095 | 309.543 | 307.88 |
GEX-FGM | 148.861 | 307.722 | 309.086 | 317.282 | 311.363 | 309.086 |
EX-FGM | 151.412 | 308.823 | 309.345 | 314.559 | 311.007 | 309.345 |
W-FGM | 149.915 | 309.829 | 311.193 | 319.389 | 313.47 | 311.193 |
φ1=0.6, φ2=3 | |||||||||
δ | −0.4 | 0.5 | |||||||
ML estimate | Bayesian estimate | ML estimate | Bayesian estimate | ||||||
n | Bias | MSE | Bias | MSE | Bias | MSE | Bias | MSE | |
20 | φ1 | 0.045 | 0.257 | 0.032 | 0.245 | 0.087 | 0.357 | 0.054 | 0.321 |
φ2 | 0.098 | 0.784 | 0.085 | 0.699 | 0.110 | 0.821 | 0.099 | 0.711 | |
δ | 0.074 | 0.457 | 0.071 | 0.416 | 0.085 | 0.524 | 0.078 | 0.511 | |
50 | φ1 | 0.038 | 0.241 | 0.029 | 0.233 | 0.078 | 0.328 | 0.042 | 0.299 |
φ2 | 0.082 | 0.698 | 0.072 | 0.587 | 0.096 | 0.745 | 0.082 | 0.621 | |
δ | 0.071 | 0.421 | 0.065 | 0.387 | 0.068 | 0.389 | 0.052 | 0.354 | |
100 | φ1 | 0.029 | 0.239 | 0.022 | 0.198 | 0.062 | 0.289 | 0.034 | 0.247 |
φ2 | 0.075 | 0.631 | 0.064 | 0.514 | 0.082 | 0.687 | 0.071 | 0.587 | |
δ | 0.067 | 0.401 | 0.059 | 0.345 | 0.056 | 0.341 | 0.048 | 0.312 | |
150 | φ1 | 0.015 | 0.214 | 0.014 | 0.167 | 0.058 | 0.245 | 0.028 | 0.187 |
φ2 | 0.064 | 0.543 | 0.052 | 0.489 | 0.075 | 0.578 | 0.068 | 0.517 | |
δ | 0.058 | 0.389 | 0.041 | 0.312 | 0.051 | 0.287 | 0.038 | 0.287 |
φ1=2, φ2=0.3 | |||||||||
δ | −0.4 | 0.5 | |||||||
ML estimate | Bayesian estimate | ML estimate | Bayesian estimate | ||||||
n | Bias | MSE | Bias | MSE | Bias | MSE | Bias | MSE | |
20 | φ1 | 0.075 | 0.547 | 0.065 | 0.511 | 0.095 | 0.655 | 0.078 | 0.625 |
φ2 | 0.109 | 0.324 | 0.099 | 0.301 | 0.125 | 0.387 | 0.134 | 0.336 | |
δ | 0.041 | 0.245 | 0.032 | 0.211 | 0.058 | 0.389 | 0.049 | 0.380 | |
50 | φ1 | 0.069 | 0.537 | 0.056 | 0.489 | 0.089 | 0.587 | 0.065 | 0.587 |
φ2 | 0.098 | 0.287 | 0.087 | 0.287 | 0.117 | 0.354 | 0.128 | 0.312 | |
δ | 0.032 | 0.231 | 0.028 | 0.201 | 0.047 | 0.345 | 0.041 | 0.311 | |
100 | φ1 | 0.058 | 0.510 | 0.048 | 0.452 | 0.074 | 0.543 | 0.052 | 0.521 |
φ2 | 0.082 | 0.241 | 0.075 | 0.265 | 0.099 | 0.299 | 0.114 | 0.278 | |
δ | 0.025 | 0.201 | 0.018 | 0.241 | 0.041 | 0.311 | 0.038 | 0.289 | |
150 | φ1 | 0.042 | 0.465 | 0.035 | 0.421 | 0.062 | 0.487 | 0.041 | 0.487 |
φ2 | 0.075 | 0.211 | 0.069 | 0.214 | 0.078 | 0.254 | 0.102 | 0.263 | |
δ | 0.021 | 0.187 | 0.012 | 0.411 | 0.032 | 0.266 | 0.024 | 0.211 |
φ1=0.5 φ2=0.7 | |||||||||
δ | −0.4 | 0.5 | |||||||
ML estimate | Bayesian estimate | ML estimate | Bayesian estimate | ||||||
n | Bias | MSE | Bias | MSE | Bias | MSE | Bias | MSE | |
20 | φ1 | 0.057 | 0.258 | 0.049 | 0.245 | 0.078 | 0.289 | 0.069 | 0.257 |
φ2 | 0.069 | 0.298 | 0.054 | 0.285 | 0.089 | 0.314 | 0.075 | 0.301 | |
δ | 0.111 | 0.435 | 0.095 | 0.411 | 0.121 | 0.542 | 0.102 | 0.521 | |
50 | φ1 | 0.048 | 0.246 | 0.035 | 0.234 | 0.069 | 0.258 | 0.053 | 0.241 |
φ2 | 0.055 | 0.278 | 0.042 | 0.262 | 0.065 | 0.295 | 0.069 | 0.278 | |
δ | 0.096 | 0.421 | 0.091 | 0.387 | 0.112 | 0.487 | 0.087 | 0.410 | |
100 | φ1 | 0.035 | 0.232 | 0.029 | 0.221 | 0.052 | 0.241 | 0.045 | 0.225 |
φ2 | 0.042 | 0.265 | 0.034 | 0.251 | 0.052 | 0.274 | 0.061 | 0.197 | |
δ | 0.085 | 0.411 | 0.081 | 0.398 | 0.089 | 0.478 | 0.085 | 0.456 | |
150 | φ1 | 0.028 | 0.211 | 0.021 | 0.202 | 0.043 | 0.234 | 0.037 | 0.215 |
φ2 | 0.031 | 0.245 | 0.021 | 0.243 | 0.035 | 0.251 | 0.052 | 0.165 | |
δ | 0.079 | 0.398 | 0.079 | 0.378 | 0.081 | 0.415 | 0.074 | 0.741 |
φ1=2 φ2=4 | |||||||||
δ | −0.4 | 0.5 | |||||||
ML estimate | Bayesian estimate | ML estimate | Bayesian estimate | ||||||
n | Bias | MSE | Bias | MSE | Bias | MSE | Bias | MSE | |
20 | φ1 | 0.086 | 0.457 | 0.075 | 0.421 | 0.092 | 0.511 | 0.084 | 0.498 |
φ2 | 0.098 | 0.578 | 0.085 | 0.543 | 0.099 | 0.654 | 0.095 | 0.625 | |
δ | 0.078 | 0.254 | 0.069 | 0.247 | 0.089 | 0.347 | 0.081 | 0.321 | |
50 | φ1 | 0.078 | 0.423 | 0.068 | 0.387 | 0.085 | 0.482 | 0.071 | 0.453 |
φ2 | 0.085 | 0.562 | 0.078 | 0.523 | 0.087 | 0.589 | 0.081 | 0.601 | |
δ | 0.069 | 0.242 | 0.061 | 0.241 | 0.081 | 0.341 | 0.074 | 0.298 | |
100 | φ1 | 0.065 | 0.387 | 0.053 | 0.354 | 0.075 | 0.421 | 0.053 | 0.421 |
φ2 | 0.074 | 0.523 | 0.062 | 0.478 | 0.072 | 0.512 | 0.063 | 0.587 | |
δ | 0.062 | 0.214 | 0.059 | 0.201 | 0.078 | 0.287 | 0.071 | 0.274 | |
150 | φ1 | 0.058 | 0.345 | 0.042 | 0.299 | 0.068 | 0.388 | 0.046 | 0.401 |
φ2 | 0.062 | 0.487 | 0.058 | 0.463 | 0.066 | 0.483 | 0.058 | 0.536 | |
δ | 0.054 | 0.203 | 0.041 | 0.189 | 0.069 | 0.251 | 0.061 | 0.232 |
−lnL | AIC | AICc | BIC | HQIC | CAIC | |
EP-EX-FGM | 150.679 | 307.358 | 307.88 | 313.095 | 309.543 | 307.88 |
GEX-FGM | 148.861 | 307.722 | 309.086 | 317.282 | 311.363 | 309.086 |
EX-FGM | 151.412 | 308.823 | 309.345 | 314.559 | 311.007 | 309.345 |
W-FGM | 149.915 | 309.829 | 311.193 | 319.389 | 313.47 | 311.193 |