A Novel Modified Alpha Power Transformed Weibull Distribution and Its Engineering Applications

1  Introduction

The Weibull distribution is a wildly favoured lifetime distribution in reliability studies. It is naturally employed for studying biological, hydrological and medical data sets. The Weibull distribution is frequently employed as a suitable alternative to well-known distributions such as exponential, gamma and inverse Weibull distributions. The random variable X is expressed to have a Weibull distribution if its probability density function (PDF) is given by

g(x;λ,θ)=λθxθ−1e−λxθ,x>0,(1)

where λ>0 and θ>0 are the scale and shape parameters respectively. Also, its cumulative distribution function (CDF) takes the form

G(x;λ,θ)=1−e−λxθ.x>0.(2)

One of the major weaknesses of the Weibull distribution is that it does not deliver an adequate fit for some applications, particularly, when the hazard rates are upside-down bathtub or bathtub shapes. To overcome this disadvantage, several investigators have developed various generalizations and modifications of the Weibull distribution to model different types of data in recent years. The generalized Weibull distribution was introduced in reference [1,2] by adding a shape parameter to the Weibull distribution. Likewise, Xie et al. [3] proposed the additive Weibull distribution, the generalized modified Weibull distribution by [4], KumaraswamyWeibull distribution by [5], beta Sarhan-Zaindin modified Weibull distribution by [6], Weibull-Weibull distribution by [7], alpha power Weibull (APW) distribution by [8], log-normal modified Weibull distribution and its reliability implications by [9], generalized extended exponential-Weibull distribution by [10], Poisson modified weibull distribution by [11], alpha logarithmic transformed Weibull distribution by [12] and logarithmic transformed Weibull by [13].

Recently, Mahdavi et al. [14] suggested a new approach to present an additional parameter to a class of distributions for more additional flexibility. The offered technique is named alpha power transformation (APT) and it is worthwhile to incorporate skewness into a family of distributions. They studied the main properties of the APT method and introduced an extension to the exponential distribution using the new approach. Many authors used the same technique to introduce new generalizations of some well-known distribution. For example, alpha power Weibull distribution by [8], alpha power generalized exponential by [15], alpha power transformed inverse Lindley distribution by [16] and alpha power Gompertz distribution by [17]. To add more flexibility to the APT method, Alotaibi et al. [18] proposed a new form of the APT method which is called the modified APT (MAPT) method. The CDF and the PDF of the MAPT method are, respectively, given by

FMAPT(x)=αG(x)−1(α−1)(1+α−αG(x)),α>0,α≠1(3)

and

fMAPT(x)=α1+G(x)log⁡(α)g(x)(α−1)(1+α−αG(x))2,α>0,α≠1,(4)

where α is a shape parameter, G(x) is a baseline distribution and g(x)=G´(x). Reference [18] investigated the major properties of the new generator. They also used the new method to develop a new version of the exponential distribution and studied some of its properties.

Modelling real data employing generalized distributions stays vital nowadays. Multiple generalized distributions have been considered and used in different domains. Nevertheless, there still remain considerable necessary issues applying real data, which are not handled by available models. The main purpose of this article is to offer a new unexplored version of the traditional Weibull distribution. To achieve this goal, we utilize the MAPT method to add a new shape parameter to the baseline CDF given by (2). We refer to the new model as modified alpha power transformed Weibull (MAPTW) distribution which contains one scale and two shape parameters. The main properties of the new model including, quantile, mixture expansion, moments, entropies and order statistics are derived. The unknown parameters are estimated using the maximum likelihood estimation method and the approximate confidence intervals (ACIs) of the unknown parameters are also constructed. We are encouraged to introduce the MAPTW distribution because

1.    It contains some well-known distributions as special cases including, exponential, Weibull and MAPT exponential (MAPTE) distributions. So, at least, the MAPTW distribution contains the main properties of these important distributions which are commonly used in modelling lifetime data. Also, the main characteristics of these distributions can be obtained directly from the MAPTW distribution.

2.    It is appropriate to model positively skewed, negatively skewed and approximately symmetric data which may not be adequately modelled by other competitive models.

3.    It can model decreasing, increasing, bathtub and upside-down then bathtub hazard rates which are often faced in real-life problems.

4.    It is indicated in Section 2 that the MAPTW distribution can be shown as a combination of the Weibull distribution. This property is especially helpful to derive the properties of the MAPTW distribution directly from the Weibull distribution.

5.    The analysis of two real data sets demonstrate that the MAPTW distribution compares satisfactorily with different competing lifetime distributions in modelling engineering data sets. As a results, the MAPTW distribution can be able to model engineering applications rather that some well known and recently proposed generalized models. This is due to the flexibility of its PDF and hazard rate function (HRF).

One of the main advantages of this distribution over many other generalized distributions is that it gives the main forms of the HRF with two shape parameters only. The same advantage can be found in its PDF which can be used to model positively and negatively skewed as well as approximately symmetric data which may not be adequately fitted by other distributions. This makes the new distribution more flexible, especially for modelling data sets when studying reliability experiments, electronics, materials, automotive industries and many engineering applications. Another motivation for this study is examining the behaviour of the maximum likelihood estimators as well as the ACIs by considering different sample sizes and different true parameter values. The rest of the paper is classified as follows: We present the MAPTW distribution in Section 2. The main properties of the MAPTW distribution are derived in Section 3. In Section 4, we discuss the maximum likelihood estimates (MLEs) as well as the ACIs of the model parameter. In Section 5, a simulation study is conducted to compare the performance of point and interval estimates. The effectiveness of the MAPTW distribution is depicted by studying two real data sets from the engineering field in Section 6. Finally, in Section 7 we conclude the paper.

2  The Modified Alpha Power Transformed Weibull Distribution

The MAPTW distribution is discussed in this section. If X has a Weibull distribution with PDF and CDF, respectively, provided by (1) and (2), then the CDF of the MAPTW distribution is given by (3).

F(x;α,λ,θ)=α1−e−λxθ−1(α−1)(1+α−α1−e−λxθ),x>0,α,θ,λ>0,α≠1,(5)

and the corresponding PDF is given by

f(x;α,λ,θ)=λθlog⁡(α)xθ−1e−λxθα2−e−λxθ(α−1)(1+α−α1−e−λxθ)2,x>0,α,θ,λ>0,α≠1,(6)

where α and θ are the shape parameters while λ is the scale parameter. It is observed that F(x;α,λ,1) reduces to the MAPTE by [18], F(x;→1,λ,1) tends to the exponential distribution, F(x;→1,λ,2) tends to the Rayleigh distribution, F(x;α,λ,2) tends to the MAPT Rayleigh distribution and F(x;→1,λ,θ) tends to the Weibull distribution. It is to mentioned hare that the MAPTW distribution can be considered as a weighted version of the APW distribution with weight function (1+α−α1−e−λxθ)−2. The reliability function (RF) and HRF of the MAPTW distribution are given, respectively, by

R(x;α,λ,θ)=1−α1−e−λxθ−1(α−1)(1+α−α1−e−λxθ)(7)

and

h(x;α,λ,θ)=λθlog⁡(α)xθ−1e−λxθα1−e−λxθ(1+α−α1−e−λxθ)(α−α1−e−λxθ).(8)

Henceforward, X is used to denote the random variable that has the PDF in (6). Fig. 1 shows the various forms of the PDF of the MAPTW distribution given by (6) by assuming different values of the shape parameters and by considering the scale parameter λ to be one in all the issues. Fig. 1 indicates that the MAPTW distribution can model the left-skewed, right-skewed and approximated symmetric data. Also, Fig. 2 presents the different shapes of the HRF given by (8) by considering various values of the shape parameters and by assuming the scale parameter to be one in all the cases. Fig. 2 reveals that the HRF of the MAPTW distribution can be decreasing, increasing, bathtub and upside-down then bathtub shaped.

Figure 1: Plots for the PDF of the MAPTW distribution

Figure 2: Plots for the HRF of the MAPTW distribution

To derive the main properties of the MAPTW distribution, we provide a valuable expansion for the PDF in (6). Consider the following power series:

αy=∑m=0∞(log⁡α)mm!ym(9)

and the generalized binomial expansion in the form

(1−y)−2=∑k=0∞(k+1)yk,|y|<1.(10)

Using the series in (9) and (10), one can write the PDF in (6) as a mixture representation of the Weibull densities as follows:

f(x)=∑m=0∞∑j=0mϖm,jg(x;(j+1)λ,θ).(11)

where g(x;(j+1)λ,θ) is the PDF of the Weibull distribution with scale parameter (j+1)λ and shape parameter θ, where

ϖm,j=(−1)j(m+1j+1)∑k=0∞α(k+1)m+1(log⁡α)m+1(m+1)!(α−1)(α+1)k+2.

Moreover, we can write the linear representation of the CDF of the MAPTW distribution after integrating (11), as follows:

FMAPTW(x)=∑m=0∞∑j=0mϖm,jG(x;(j+1)λ).(12)

where G(x;(j+1)λ) is the CDF of the Weibull distribution with scale and shape parameters (j+1)λ and θ, respectively.

3  Structure Properties

We provide some essential properties for the MAPTW distribution in this section, such as quantile, moments, entropies, order statistics, residual life, and reversed failure rate function.

3.1 Quantile Function

The quantile function (QF) of the MAPTW distribution, say x=Q(p), can be obtained from the CDF in (5) as

xp=Q(p)={−1λlog⁡[1−[log⁡(1+p(α2−1)1+p(α−1))log⁡α]]}1θ,0<p<1.(13)

The QF in (13) can be used to generate a random sample from the MAPTW distribution by taking p to be U(0,1).

3.2 Moments

Moments play an essential feature in statistical theory and numerous necessary characteristics of any distribution can be examined via moments. For the MAPTW distribution, the nth moment can be derived as

μn′=E(Xn)=∫−∞∞xnf(x)dx=∑m=0∞∑j=0mϖm,j∫0∞xng(x;(j+1)λ)dx=∑m=0∞∑j=0mϖm,jΓ(nθ+1)[(j+1)λ]nθ,n=1,2,3,…,(14)

where Γ(.) is the gamma function. Similarly, for the MAPTW distribution we can obtain the nth inverse moment by using (6) as follows:

E(1Xn)=∑m=0∞∑j=0mϖm,jΓ(1−nθ)[(j+1)λ]nθ,nθ≥1.

Practically, the mean of the MAPTW distribution can be obtained from (14) by setting n=1 as follows:

μ1′=E(X)=∑m=0∞∑j=0mϖm,jΓ(1θ+1)[(j+1)λ]1θ.

Similarly, the second moment can be obtained by putting n=2 in (14) as

μ2′=E(X2)=∑m=0∞∑j=0mϖm,jΓ(2θ+1)[(j+1)λ]2θ

Then, once can obtain the variance of the MAPTW distribution using the following relation:

Var(X)=μ2′−μ1′2.

The rth central moment μr of X is derived as Moreover, The skewness (Sk) and kurtosis (Ku) measures can be calculated employing the next formulas

Sk=μ3[Var(X)]32

and

Ku=μ4[Var(X)]2,

where μ3=μ3′−3μ2′μ1′+2μ1′3 and μ4=μ4′−4μ3′μ1′+6μ2′μ1′2−3μ1′4 are the third and fourth central moments, receptively. Based on the above measures of mean, variance, skewness and kurtosis and for various values for α and θ with λ=1, Fig. 3 displays the plots for the mean, variance, skewness and kurtosis of the MAPTW distribution. It is seen from Fig. 3 the skewness of the MAPTW distribution can be positive or negative and decreases as α and θ increase. It is also observed that the kurtosis of the MAPTW distribution decreases as α and θ increase.

Figure 3: Mean, variance, skewness and kurtosis of the MAPTW distribution

The following Lemma explain various kinds of moments of the MAPTW distribution such as incomplete moments (IMs), moment generating function (MGF), characteristic generating function (CGF) and conditional moments (CMs).

Lemma 3.1. If X ∼ MAPTIW, then

1.    The IMs of X is

ψr(t)=∑m=0∞∑j=0mϖm,jγ(r+1,(j+1)λtθ)[(j+1)λ]rθ,

where γ(.,.) is the incomplete gamma function.

2.   The MGF of X is

MX(t)=∑k=0∞tkk!E(Xk)=∑k,m=0∞∑j=0mϖm,jtkk!Γ(kθ+1)[(j+1)λ]kθ.

3.   The CGF of X is

MX(it)=∑k=0∞(it)kk!E(Xk)=∑k,m=0∞∑j=0mϖm,j(it)kk!Γ(kθ+1)[(j+1)λ]kθ.

4.   The CMs of X is

CX(t)=E(Xn|X>x)=ψn(x)1−F(x),

3.3 Entropies

Entropy is one of the numerous popular techniques used to calculate the uncertainty corresponding to a random variable and it was initially employed in the field of physics. Estimating entropy is a significant matter in many fields including statistics and biological phenomenon. High entropy is guided to minor information found in data. For the MAPTW distribution and from (6), the Renyi entropy (RE) can be acquired as follows:

Iρ(x)=11−ρlog⁡{(λθlog⁡αα−1)ρ∫0∞xθα(2+e−λxθ)ρe−λρxθ(α−(α+1)αe−λxθ)2ρdx}=11−ρlog⁡{(λθlog⁡αα−1)ρ∑j=0∞∑m=0∞(2ρ+j−1j)(α+1α)j(j+ρ)m(log⁡α)mm!Γ(1θ+1)[λ(m+ρ)]1θ}.(15)

Also, Shannon’s entropy can be derived by Limiting ρ↑1 in (15) as

E[−log⁡f(X)]=−log⁡[λθlog⁡αα−1]−(θ−1)E(log⁡(x))+λE(xθ)+log⁡(α)E(2−e−λxθ)−2E(log⁡[1+α−α1−e−λxθ])=−log⁡[λθlog⁡αα−1]+∑m=0∞∑j=0∞ϖm,j(θ−1)(log⁡((j+1)λ)+γ)θ+∑m=0∞∑j=0∞ϖm,j(1+j)λ+log⁡(α)∑m=0∞∑j=0∞ϖm,j(j+3j+2)−2E(log⁡[1+α−α1−e−λxθ])

3.4 Order Statistics

Order statistics contain a broad range of applications in statistics, including nonparametric statistics, life testing, quality control monitoring and reliability analysis. Let X1,…,Xn be a random sample from the MAPTW distribution with CDF and PDF given by (5) and (6), respectively. Suppose also that the order statistics of X1,…,Xn are denoted by X(1),…,X(n). Then, the PDF of the ith order statistic, X(i), is expressed as

fX(i)(x)=f(x)B(i,n−i+1)Fi−1(x)[1−F(x)]n−i=f(x)B(i,n−i+1)∑k=0n−i(−1)k(n−ik)Fk+i−1(x),(16)

where B(.,.) is the beta function. Using (5), (6) and a power series expansion, we obtain

f(x)Fk+i−1(x)=αlog⁡α(α2−1)i+k∑a,b,c,d=0∞(−1)i+k+b+d−1(α+1)a+1((a+b+1)log⁡α)cc!(a+i+ka)×(i+k−1b)(cd)λθxθ−1e−λ(d+1)xθ,(17)

Substituting (17) in Eq. (16), the PDF of X(i) reduces to

fX(i)(x)=∑d=0∞ωdg(x;(d+1)λ,θ)(18)

where g(x;(d+1)λ,θ) is the PDF of the Weibull distribution with scale parameter (d+1)λ and shape parameter θ, where

ωd=∑k=0n−i∑a,b,c=0∞(−1)i+2k+b+d−1(α+1)a+1αlog⁡α(α2−1)i+kB(i,n−s+1)((a+b+1)log⁡α)cc!×(a+i+ka)(i+k−1b)(n−ik)(cd).

3.5 Probability Weighted Moments

The (u,v)th probability weighted moments can be defined as

ϑu,v=E{XuF(X)v}=∫−∞∞xuf(x)Fv(X)dx.(19)

For the of the MAPTW distribution, it follows from (5) and (6) that

f(x)Fv(x)=αlog⁡α(α2−1)v+1∑a,b,c,d=0∞(−1)v+b+d(α+1)a+1((a+b+1)log⁡α)cc!(a+v+1a)×(vb)(cd)λθxθ−1e−λ(d+1)xθ,(20)

Substituting (20) in Eq. (19), the probability weighted moments of the MAPTW distribution is given by

ϑu,v=∑a,b,c,d=0∞(−1)v+b+d(α+1)a+1αlog⁡α(α2−1)v+1[(a+b+1)log⁡α]cc!(a+v+1a)×(vb)(cd)Γ(1+uθ)θ[(1+d)λ]1+uθ

3.6 Residual Life and Reversed Failure Rate Function

The rth moment of the residual life (RL) of the random variable X is defined as follows:

Rr(t)=E((X−t)r|X>t)=11−F(t)∫t∞(x−t)rf(x)dx,r≥1

Using (6) and applying the binomial expansion of (x−t)r, we have

Rr(t)=11−F(t)∑k=0r(−t)r−k(rk)∫t∞xrf(x)dx=11−F(t)∑k=0r∑m,j=0∞ϖm,j(−t)r−k(rk)∫t∞xrg(x;(j+1)λ,θ)dx

where g(x;(j+1)λ,θ) is the PDF of the Weibull distribution, then

Rr(t)=(α−1)(1+α−α1−e−λxθ)α2+(α−2)α1−e−λxθ−1∑k=0r∑m,j=0∞ϖm,j(−t)r−k(rk)Γ(rθ+1,(j+1)λtθ)[(j+1)λ]rθ,

where Γ(a,y)=∫y∞za−1e−zdz denotes the incomplete gamma function. The rth moment of the reversed RL can be derived using the general formula

mr(t)=E((t−X)r|X≤t)=1F(t)∫0t(t−x)rf(x)dx,r≥1.

Using (5), (6) and applying the binomial expansion of (t−x)r, we can write

mr(t)=(α−1)(1+α−α1−e−λxθ)α1−e−λxθ−1∑k=0r∑m,j,a=0∞ϖm,j(−1)a+r−k(rk)θ((j+1)λ)a+1t2r−k+(a+1)θr+(a+1)θ

3.7 Stress-Strength Model

Let Y1∼MAPTW(α1,θ,λ) and Y2∼MAPTW(α2,θ,λ). If Y1 represents stress and Y2 represents strength, then the stress-strength parameter, denoted by R, for the MAPTW distribution. Here, we consider two cases as follows:

Case one: When α1≠α2

R=P(Y2>Y1)=∫0∞F1(y)dF2(y)dy=λθlog⁡(α2)(α1−1)(α1+1)(α2−1)(α2+1)2∫0∞yθ−1e−λyθα22−e−λyθ(α11−e−λyθ−1)∗×(1−α11−e−λyθα1+1)−1(1−α21−e−λyθα2+1)−2dy

Using the series expansion in (10), the last equation takes the form

R=∑j=0∞∑k=0∞T1(j,k)∫0∞λθyθ−1e−λyθ(α1α1e−(j+1)λyθ−α1e−jλyθ)α2−(k+1)e−λyθdy.

Employing the series expansion in (9), we obtain

R=∑j=0∞∑k=0∞T1(j,k)∫0∞λθyθ−1e−λyθ(α1e−λyθ−1)α1je−λyθα2−(k+1)e−λyθdy=∑j,k=0∞∑n,m=0∞T1(j,k)T2(n,m)∫0∞λθyθ−1e−(n+m+1))λyθdy=∑j,k=0∞∑n,m=0∞T1(j,k)T2(n,m)n+m+1

where

T1(j,k)=(k+1)α2k+2α1jlog⁡(α2)(α1−1)(α2−1)(α1+1)j+1(α2+1)k+2

and

T2(n,m)=(−1)n+m(k+1)m(log⁡(α1))n(log⁡(α2))mn!m!(α1(j+1)n−jn).

Case two: When α1≠α2, λ1≠λ2 and θ1≠θ2

Let Y1∼MAPTW(α1,θ1,λ1) and Y2∼MAPTW(α2,θ1,λ2). If Y1 represents stress and Y2 represents strength, then the stress-strength parameter, denoted by R, for the MAPTW distribution is given by

R=P(Y2>Y1)=∫0∞F1(y)dF2(y)dy=λ2θ2log⁡(α2)(α1−1)(α1+1)(α2−1)(α2+1)2∫0∞yθ2−1e−λ2yθ2α22−e−λ2yθ2(α11−e−λ1yθ1−1)×(1−α11−e−λ1yθ1α1+1)−1(1−α21−e−λ2yθ2α2+1)−2dy

Using the series expansion in (10), we have

R=∑j=0∞∑k=0∞T1(j,k)∫0∞λ2θ2yθ2−1e−λ2yθ2(α11−e−λ1yθ1−1)α1−je−λ1yθ1α2−(k+1)e−λ2yθ2dy.

Utilizing the series expansion in (9), it comes

R=∑j=0∞∑k=0∞T1(j,k)∫0∞λ2θ2yθ2−1e−λ2yθ2(α11−e−λ1yθ1−1)α1−je−λ1yα2−(k+1)e−λ2yθ2dy=∑j,k=0∞∑n,m=0∞T1(j,k)T2(n,m)∫0∞λ2θ2yθ2−1e−(nλ1yθ1+(m+1)λ2yθ2)dy,

where

T1(j,k)=(k+1)α1jα22+klog⁡(α2)(α1−1)(α2−1)(α1+1)j+1(α2+1)k+2

and

T2(n,m)=(−1)n+m(k+1)m(log⁡(α1))n(log⁡(α2))mn!m!(α1(j+1)n−jn).

4  Estimation of the Parameters

Here, the method of maximum likelihood is employed to estimate the unknown parameters of the MAPTW distribution. Moreover, the approximate confidence intervals (ACIs) of the unknown parameters are obtained. Assume that we have a random sample of size n taken from the MAPTW distribution with PDF given by (6), then we can express the log-likelihood function in this case as follows:

ℓ(α,λ,θ)=nlog⁡(λθlog⁡αα−1)+(θ−1)∑i=1nlog⁡xi−λ∑i=1nxiθ+(log⁡α)∑i=1n(2−e−λxiθ)−2∑i=1nlog⁡(1+α−α1−e−λxiθ).(21)

To get the maximum likelihood estimates (MLEs) of α, λ and θ denoted by α^, λ^ and θ^, one can maximize the objective function in (21) with respect to α, λ and θ. An alternative approach to compute the required estimates is to solve the following normal equations simultaneously:

∂ℓ(α,λ,θ)∂α=nαlog⁡(α)−nα−1+1α∑i=1n(2−e−λxiθ)−2∑i=1nφα=0,(22)

∂ℓ(α,λ,θ)∂λ=nλ−∑i=1nxiθ+log⁡(α)∑i=1nxiθe−λxiθ−2∑i=1nφλ=0(23)

and

∂ℓ(α,λ,θ)∂θ=nθ−2∑i=1nφθ+∑i=1nlog⁡(xi)[1+λxiθ(log⁡(α)e−λxiθ−1)]=0,(24)

where

φα=1−(1−e−λxiθ)α−e−λxiθα−α1−e−λxiθ+1,

φλ=−log⁡(α)xiθe−λxiθα1−e−λxiθα−α1−e−λxiθ+1

and

φθ=−αλlog⁡(α)xiθlog⁡(xi)e−λxiθ(α+1)αe−λxiθ−α.

It is to note that there are no closed-form solutions for Eqs. (22)–(24). To get the MLEs in this case, one can direct to any numerical technique for this purpose. Using the asymptotic properties of the MLEs, we can obtain the ACIs of α, λ and θ. It is known that (α,λ,θ)∼N((α^,λ^,θ^),I0−1(α,λ,θ)), where I0−1(α,λ,θ) is the asymptotic variance-covariance (AVC) of the MLEs. Practically, it is not easy to compute I0−1(α,λ,θ), thus, the approximate AVC of the MLEs denoted by I0−1(α^,λ^,θ^) can be used as follows:

I0(α^,λ^,θ^)=[−Iαα−Iαλ−Iαθ−Iαλ−Iλλ−Iλθ−Iαθ−Iλθ−Iθθ](α,λ,θ)=(α^,λ^,θ^)−1=[var^(α^)cov^(α^,λ^)cov^(α^,θ^)cov^(λ^,α^)var^(λ^)cov^(λ^,θ^)cov^(θ^,α^)cov^(θ^,λ^)var^(θ^)].(25)

where Iαα,Iαλ=Iλα,Iαθ=Iθα,Iλθ=Iθλ,Iλλ and Iθθ are the second derivatives of (21) and given by

Iαα=n[1(α−1)2−log⁡(α)+1α2log2⁡(α)]−1α2∑i=1n(2−e−λxiθ)−2∑i=1nφαα,

Iλλ=−nλ2−log⁡(α)∑i=1nxi2θe−λxiθ−2∑i=1nφλλ,

Iθθ=nθ2+λlog⁡(α)∑i=1n[xiθ(1−xiθ)e−λxiθlog2⁡(xi)]−λ∑i=1nxiθlog2⁡(xi)−2∑i=1nφθθ,

Iαλ=∑i=1nxiθe−λxiθα−2∑i=1nφαλ,

Iαθ=1α∑i=1ne−λxiθλxiθlog⁡(xi)−2∑i=1nφαθ

and

Iλθ=−∑i=1nxiθlog⁡(xi)−log⁡(α)∑i=1nxiθlog⁡(xi)e−λxiθ(λxiθ−1)−2∑i=1nφλθ,