# Bayesian Analysis of Cancer Data Using a 4-Component Exponential Mixture Model

Authors: Farzana Noor; Saadia Masood; Yumna Sabar; Syed Bilal Hussain Shah; Touqeer Ahmad; Asrin Abdollahi; Ahthasham Sajid
Journal: Computational and Mathematical Methods in Medicine
 (2021)

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## Abstract

Cancer is among the major public health problems as well as a burden for Pakistan. About 148,000 new patients are diagnosed with cancer each year, and almost 100,000 patients die due to this fatal disease. Lung, breast, liver, cervical, blood/bone marrow, and oral cancers are the most common cancers in Pakistan. Perhaps smoking, physical inactivity, infections, exposure to toxins, and unhealthy diet are the main factors responsible for the spread of cancer. We preferred a novel four-component mixture model under Bayesian estimation to estimate the average number of incidences and death of both genders in different age groups. For this purpose, we considered 28 different kinds of cancers diagnosed in recent years. Data of registered patients all over Pakistan in the year 2012 were taken from GLOBOCAN. All the patients were divided into 4 age groups and also split based on genders to be applied to the proposed mixture model. Bayesian analysis is performed on the data using a four-component exponential mixture model. Estimators for mixture model parameters are derived under Bayesian procedures using three different priors and two loss functions. Simulation study and graphical representation for the estimates are also presented. It is noted from analysis of real data that the Bayes estimates under LINEX loss assuming Jeffreys’ prior is more efficient for the no. of incidences in male and female. As far as no. of deaths are concerned again, LINEX loss assuming Jeffreys’ prior gives better results for the male population, but for the female population, the best loss function is SELF assuming Jeffreys’ prior.


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## Body

## 1. Introduction

Most people think that there is no fatal diagnosis other than that of cancer. However, this may be an exaggerated and overgeneralized vision of cancer. But, it is always admitted as a serious life-threatening disease. Cancer is considered a major cause of death among deaths due to the noncontagious diseases in Pakistan. Even then, we do not find any study from Pakistan on cancer-specific incidence and mortality rates based on age groups. In the last 25 years, Pakistan had observed a significant increase in the number of various kinds of cancer cases. In Pakistan, there is no work done in the field of cancer using Bayesian analysis. In this study, we have conducted a Bayesian analysis of data about the survival rate of cancer patients through a mixture model.Intercellular communication and culture conditions are the main supporter in the formation of cancer cells [1], but no specific cause for cancer can be identified [2]. In Pakistan, cancer is a major health problem. The use of tobacco, the growing and aging population, and the westernization diet are the various factors that tend to increase cancer cases. Studies reveal that every year, nearly 300,000 various new kinds of cancer cases are reported from all over the country. Everyone can be affected by cancer at any age. Early detection, diagnosis, treatment, and especially awareness are very important to stop and prevent the disease. At least one-third to forty percent of all cancer cases are preventable merely by not using tobacco, using healthy diet, and being physically active with at least a 30-minute workout daily. Some important studies on thyroid cancer include [3–5].The exponential distribution is often used as a model for durations and is particularly applied to find out the lifetimes of objects whose life is not dependent on their ages. Therefore, the exponential model is deemed appropriate and is popular to model the length of life for electronic objects.Finite mixture models find great importance and application in a wide variety of statistical phenomena. In the past decade, the applications of finite mixture models have broadened significantly. The mixture models are convenient when we require splitting the whole population into subpopulations. Titterington et al. [6], Everitt and Hand [7], and McLachlan and Peel [8] have provided a valuable account of information on analysis and applications of mixtures. A mixture model is simply a weighted sum of component densities, and mathematically, it can be written as
(1)fyπ=∑j=1mpjfjyπjwherepj>0j=1,2,⋯,mand∑j=1mpj=1.Here,fj and pj represent component densities and weight factors, respectively. A mixture model generally may be composed of several components which can take the same or a different distributional form. Simplicity is obtained if the mixture model is composed of the same distributions.Many authors have considered estimation of mixture models in their work such as McCullagh [9] who generates a mixture of linear exponential models using quadratic and exponential models. Abu-Taleb et al. [10] present Bayes estimation for the parameters of the lifetime distribution when both censoring and survival time are exponentially distributed. Noor et al. [11] have analyzed a mixture model by mixing Rayleigh and Burr XII distribution under a Bayesian setup. Abu Zinadah [12] presents maximum likelihood estimation and Bayesian analysis on exponential distribution and exponential pareto under type II censoring. Feroze and Aslam [13] have considered the Bayesian analysis of Burr type X distribution. Noor and Aslam [14] present the Bayesian analysis for the mixture of two inverse Weibull models. Tsutakawa [15] applies the Bayesian technique for assessing death rates of cancer when the recurrence of passing over a predefined era is expected to have Poisson distribution. Lambert et al. [16] consider a study of population-based cancer. They expand the parametric nonmixture cure part and accordingly give estimates of the cure division in population-based cancer. Hamdi [17] considers Bayesian statistical modelling in Ohio State for count data on cancer death.Censoring is inevitable in experiments related to the life testing of some subjects/objects. A sample is a censored sample whenever it does not contain full information due to some experimental conditions. For example, a lung cancer patient is enrolled for a clinical trial to test the effect of a drug on his survival from his disease. But, he died in a car accident afterT years of his disease. His survival with lung cancer is at least T years, but the exact years cannot be known. Though researchers have introduced/used different censoring schemes such as right, left, type I or type II censoring, and interval censoring, but right censoring is mostly used in life testing, see Cohen [18] for details on the censoring.By introducing a 4-component exponential mixture model, the objective of the study is to contribute to the widest spreading field of mixture models and provide its application to cancer data. The Bayesian technique is opted to analyze the mixture model. Bayesian analysis is performed using different priors and loss functions assuming data is right-censored. Mainly, the paper is designed in the following manner:Materials and Methods contains a four-component exponential mixture model, likelihood, posterior densities using informative prior (IP) and noninformative prior (NIP), Bayes estimators, and posterior risks. In Results and Discussions, simulated and real-life data results are presented. Finally, the conclusion of the study is presented.


## 2. Materials and Methods

### 2.1. Component Mixture of Exponential Distributions and Likelihood Function

Let a random variableY be exponentially distributed with parameter πl, with probability density function:
(2)fly;πl=πle−πly,y≥0,πl>0,l=1,2,3,4.The parameterπ represents the rate at which an event occurs.And the c.d.f is given as(3)Fly=1−e−πly.Thus, a mixture model following a 4-component density which assumes exponential distributions with unknown mixing proportionsp1,p2,andp3 may take the form:
(4)fy;π1,π2,π3,π4,p1,p2,p3=p1π1e−π1y+p2π2e−π2y+p3π3e−π3y+1−p1−p2−p3π4e−π4y.And the c.d.f of the mixture model is(5)Fy=1−p1e−π1y−p2e−π2y−p3e−π3y−1−p1−p2−p3e−π4y.Let an experiment for testing lifetimes of some objects withn units is performed for the 4-component mixture model. It is assumed that, for a prespecified time, the experimenter will get s units failed and the remaining n−s units are removed from the experiment without knowing their lifetime and population as well. These failed s units are classified as s1,s2,s3, and s4 that can be assigned to respective subpopulations after knowing the cause of their failure according to Mendenhall and Hader [19] such that s=s1+s2+s3+s4. Now, define ysl,0<ysl<t, as the failure time of lth, l=1,2,⋯,si unit belongs to sth,s=1,2,3,4 subpopulation. Thus, the likelihood function of the 4-component mixture model for the random variable y is given as
(6)Lρ∣y∝∏r=1s1p1f1y1l∏r=1s2p2f2y2l∏r=1s3p3f3y3l∏r=1s41−p1−p2−p3f4y4l1−Ftn−s,Lρ∣y∝π1π2π3π4∑u=0n−s∑v=0u∑w=0vn−suuvvwexp−π1nt−st−ut+∑l=1s1y1lexp−π2ut−vt+∑l=1s2y2lexp−π3vt+∑l=1s3y3lexp−π4∑l=1s4y4l,where ρ=π1,π2,π3,π4,p1,p2,p3andy=y11,⋯,y1s1,y21,⋯,y2s2,y31,⋯,y3s3,y41,⋯,y4s4.


### 2.2. The Posterior Distribution Using IP

Gamma distribution is used as prior for component parametersπ1,π2,π3,andπ4, and bivariate beta distribution is chosen prior for proportion parameters p1,p2,andp3, i.e.,
(7)δiπi;ci,di=diciΓciπici−1expdiπi,πi>0,ci,di>0,i=1,2,3,4,δ5p1,p2,p3;e1,e2,e3,e4=1Be1,e2,e3p1e1−1p2e2−1p3e3−11−p1−p2−p3e4−1,p1,p2,p3≥0,p1+p2+p3≤1,e1,e2,e3,e4>0.The joint prior distribution of parametersπ1,π2,π3,π4,p1,p2,andp3 using the IP is
(8)δ6ρ∣y∝π1c1−1expd1,π1π2c2−1expd2,π2π3c3−1expd3,π3π4c4−1expd4,π4p1e1−1p2e2−1p3e3−11−p1−p2−p3e4−1.The joint posterior distribution of parametersπ1,π2,π3,π4,p1,p2,andp3 using the IP is
(9)gρy=Lρyδ6ρy∫0ρLρyδ6ρydρ,g1ρy=Q1−1∑u−0n−s∑v=0u∑w=0vn−suuvvwπ1J11−1π2J12−1π3J13−1π4J14−1exp−π1K11exp−π2K12exp−π3K13exp−π4K14p1E01−1p2F01−1p3G01−11−p1−p2−p3H01−1,where
(10)J11=s1+c1,J12=s2+c2,J13=s3+c3,J14=s4+c4,K11=nt−st−ut+∑l=1s1y1l+d1,K12=ut−vt+∑l=1s2y2l+d2,K13=vt−wt+∑l=1s3y3l+d3,K14=wt+∑l=1s4y4l+d4,E01=n−s−u+s1+e1,F01=u−v+s2+e2,G01=v−w+s3+e3,H01=w+s4+e4,Q1=J11J12J13J14∑u=0n−s∑v=0u∑w=0vn−suuvvwK11−J11K12−J12K13−J13K14−J14BE01,F01,G01,H01.


### 2.3. The Posterior Distribution Using NIP

According to Jeffrey [20], Jeffreys’ prior is defined as
(11)pπm∝Iπm,m=1,2,3,4,where Iπm=−E∂2fy∣πm/∂πm2 is Fisher’s information matrix.It is assumed that parametersπ1,π2,π3,andπ4 follow Jeffreys’ prior while mixing proportions p1,p2,andp3 assume a uniform prior over an interval 0,1. Thus, the joint prior distribution of parameters is given by
(12)δ7ρy∝1π1π2π3π4,π1,π2,π3,π4>0,p1,p2,p3>0,p1+p2+p3≤1.So, the posterior distribution ofπ1,π2,π3,π4,p1,p2,andp3 jointly using the JP is
(13)g2ρ∣y=Q2−1∑u−0n−s∑v=0u∑w=0vn−suuvvwπ1J21−1π2J22−1π3J23−1π4J24−1exp−π1K21exp−π2K22exp−π3K23exp−π4K24p1E02−1p2F02−1p3G02−11−p1−p2−p3H02−1,where J21=s2, J22=s2, J23=s3, J24=s4, K21=nt−st−ut+∑l=1s1y1l, K22=ut−vt+∑l−1s2y2l, K23=vt−wt+∑l=1s3y3l, K24=wt+∑l=1s4y4l, E01=n−s−u+s1+1, F01=u−v+s2+1, G01=v−w+s3+1, H01=w+s4+1, Q2=J21J22J23J24∑u=0n−s∑v=0u∑w=0vn−suuvvwK21−J21K22−J22K23−J23K24−J24BE02,F02,G02,H02.


### 2.4. The Posterior Distribution Using Jeffreys’ Gamma Prior

The joint prior density ofπ1,π2,π3,andπ4 under Jeffreys’ prior is defined earlier in (12).Now, suppose(14)πi∼JGammaαi,βi,i=1,2,3,4,pi∼U0,1.Considering the independence of priors, we get a joint prior as(15)δ8ρy∝π1α1−1−1e−β1π1π2α2−1−1e−β2π2π3α3−1−1e−β3π3π4α4−1−1e−β4π4.So, the joint posterior distribution of parametersπ1,π2,π3,π4,pi using JGamma prior is
(16)g3ρ∣y=Q3−1∑u=0n−s∑v=0u∑w=0vn−suuvvwπ1J31−1π2J32−1π3J33−1π4J34−1exp−π1K31exp−π2K32exp−π3K33exp−π4K34p1E03−1p2F03−1p3G03−11−p1−p2−p3H03−1,where Q3=J31J32J33J34∑u=0n−s∑v=0u∑w=0vn−suuvvwK31−J31K32−J32K33−J33K34−J34BE03,F03,G03,H03, J31=s1+α1−1, J32=s2+α2−1, J33=s3+α3−1, J34=s4+α4−1, K31=K1+k1, K32=K2+k2, K33=K3+k3,K34=K4+k4, E03=n−s−u+s1+1, F03=u−v+s2+1, G03=v−w+s3+1, H03=w+s4+1.


### 2.5. Bayes Estimators and Posterior Risks Using IP, JP, and JG under SELF and LINEX

#### 2.5.1. Loss Functions

Letd^ is the Bayes estimator then ELπ,d^ is its posterior risk. Our purpose, in this study, is to check out the properties of derived estimators and look for efficient loss functions using different priors. Two different loss functions, namely, SELF and LINEX, are used to obtain the Bayes estimators and their posterior risks. The SELF is defined as Ld,π=d−π2. The LINEX loss function can be defined as Lπ^,π=expcπ^−π−cπ^−π−1,c≠0.One can get a Bayes estimator with associated posterior risk under SELF asd^=Eπ and τd^=Eπ2−Eπ2. Similarly, using LINEX, loss Bayes estimators and posterior risks can be obtained by π^=−1/clogEposte−cπ and Riskπ^LINEX=lnEcπ+cEπ. The Bayes estimators and posterior risks under IP, JP, and JG for parameters π1,π2,π3,π4,p1,p2, and p3 under SELF and LINEX are obtained as
(17)π^h1=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1+1Jh2Jh3Jh4Kh1−Jh1+1Kh2−Jh2Kh3−Jh3Kh4−Jh4BE0h,F0h,G0h,H0h,(18)π^h2=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2+1Jh3Jh4Kh1−Jh1Kh2−Jh2+1Kh3−Jh3Kh4−Jh4BE0h,F0h,G0h,H0h,(19)π^h3=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2Jh3+1Jh4Kh1−Jh1Kh2−Jh2Kh3−Jh3+1Kh4−Jh4BE0h,F0h,G0h,H0h,(20)π^h4=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2Jh3Jh4+1Kh1−Jh1Kh2−Jh2Kh3−Jh3Kh4−Jh4+1BE0h,F0h,G0h,H0h,(21)p^h1=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2Jh3Jh4Kh1−Jh1Kh2−Jh2Kh3−Jh3Kh4−Jh4BE0h+1,F0h,G0h,H0h,(22)p^h2=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2Jh3Jh4Kh1−Jh1Kh2−Jh2Kh3−Jh3Kh4−Jh4BE0h,F0h+1,G0h,H0h,(23)p^h3=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2Jh3Jh4Kh1−Jh1Kh2−Jh2Kh3−Jh3Kh4−Jh4BE0h,F0h,G0h+1,H0h,(24)τhπ^h1=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1+2Jh2Jh3Jh4Kh1−Jh1+2Kh2−Jh2Kh3−Jh3Kh4−Jh4BE0h,F0h,G0h,H0h−π^h12,(25)τhπ^h2=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2+2Jh3Jh4Kh1−Jh1Kh2−Jh2+2Kh3−Jh3Kh4−Jh4BE0h,F0h,G0h,H0h−π^h22,(26)τhπ^h3=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2Jh3+2Jh4Kh1−Jh1Kh2−Jh2Kh3−Jh3+2Kh4−Jh4BE0h,F0h,G0h,H0h−π^h32,(27)τhπ^h4=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2Jh3Jh4+2Kh1−Jh1Kh2−Jh2Kh3−Jh3Kh4−Jh4+2BE0h,F0h,G0h,H0h−π^h42,(28)τhp^h1=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2Jh3Jh4Kh1−Jh1Kh2−Jh2Kh3−Jh3Kh4−Jh4BE0h+2,F0h,G0h,H0h−p^h12,(29)τhp^h2=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2Jh3Jh4Kh1−Jh1Kh2−Jh2Kh3−Jh3Kh4−Jh4BE0h,F0h+2,G0h,H0h−p^h22,(30)τhp^h3=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2Jh3Jh4Kh1−Jh1Kh2−Jh2Kh3−Jh3Kh4−Jh4BE0h,F0h,G0h+2,H0h−p^h32,where h=1 for the IP, h=2 for the JP, and h=3 for the JG. The Bayes estimators and posterior risks using IP, JP, and JG prior under LINEX are also derived and are presented in Appendix A.


## 2.1. Component Mixture of Exponential Distributions and Likelihood Function

Let a random variableY be exponentially distributed with parameter πl, with probability density function:
(2)fly;πl=πle−πly,y≥0,πl>0,l=1,2,3,4.The parameterπ represents the rate at which an event occurs.And the c.d.f is given as(3)Fly=1−e−πly.Thus, a mixture model following a 4-component density which assumes exponential distributions with unknown mixing proportionsp1,p2,andp3 may take the form:
(4)fy;π1,π2,π3,π4,p1,p2,p3=p1π1e−π1y+p2π2e−π2y+p3π3e−π3y+1−p1−p2−p3π4e−π4y.And the c.d.f of the mixture model is(5)Fy=1−p1e−π1y−p2e−π2y−p3e−π3y−1−p1−p2−p3e−π4y.Let an experiment for testing lifetimes of some objects withn units is performed for the 4-component mixture model. It is assumed that, for a prespecified time, the experimenter will get s units failed and the remaining n−s units are removed from the experiment without knowing their lifetime and population as well. These failed s units are classified as s1,s2,s3, and s4 that can be assigned to respective subpopulations after knowing the cause of their failure according to Mendenhall and Hader [19] such that s=s1+s2+s3+s4. Now, define ysl,0<ysl<t, as the failure time of lth, l=1,2,⋯,si unit belongs to sth,s=1,2,3,4 subpopulation. Thus, the likelihood function of the 4-component mixture model for the random variable y is given as
(6)Lρ∣y∝∏r=1s1p1f1y1l∏r=1s2p2f2y2l∏r=1s3p3f3y3l∏r=1s41−p1−p2−p3f4y4l1−Ftn−s,Lρ∣y∝π1π2π3π4∑u=0n−s∑v=0u∑w=0vn−suuvvwexp−π1nt−st−ut+∑l=1s1y1lexp−π2ut−vt+∑l=1s2y2lexp−π3vt+∑l=1s3y3lexp−π4∑l=1s4y4l,where ρ=π1,π2,π3,π4,p1,p2,p3andy=y11,⋯,y1s1,y21,⋯,y2s2,y31,⋯,y3s3,y41,⋯,y4s4.


## 2.2. The Posterior Distribution Using IP

Gamma distribution is used as prior for component parametersπ1,π2,π3,andπ4, and bivariate beta distribution is chosen prior for proportion parameters p1,p2,andp3, i.e.,
(7)δiπi;ci,di=diciΓciπici−1expdiπi,πi>0,ci,di>0,i=1,2,3,4,δ5p1,p2,p3;e1,e2,e3,e4=1Be1,e2,e3p1e1−1p2e2−1p3e3−11−p1−p2−p3e4−1,p1,p2,p3≥0,p1+p2+p3≤1,e1,e2,e3,e4>0.The joint prior distribution of parametersπ1,π2,π3,π4,p1,p2,andp3 using the IP is
(8)δ6ρ∣y∝π1c1−1expd1,π1π2c2−1expd2,π2π3c3−1expd3,π3π4c4−1expd4,π4p1e1−1p2e2−1p3e3−11−p1−p2−p3e4−1.The joint posterior distribution of parametersπ1,π2,π3,π4,p1,p2,andp3 using the IP is
(9)gρy=Lρyδ6ρy∫0ρLρyδ6ρydρ,g1ρy=Q1−1∑u−0n−s∑v=0u∑w=0vn−suuvvwπ1J11−1π2J12−1π3J13−1π4J14−1exp−π1K11exp−π2K12exp−π3K13exp−π4K14p1E01−1p2F01−1p3G01−11−p1−p2−p3H01−1,where
(10)J11=s1+c1,J12=s2+c2,J13=s3+c3,J14=s4+c4,K11=nt−st−ut+∑l=1s1y1l+d1,K12=ut−vt+∑l=1s2y2l+d2,K13=vt−wt+∑l=1s3y3l+d3,K14=wt+∑l=1s4y4l+d4,E01=n−s−u+s1+e1,F01=u−v+s2+e2,G01=v−w+s3+e3,H01=w+s4+e4,Q1=J11J12J13J14∑u=0n−s∑v=0u∑w=0vn−suuvvwK11−J11K12−J12K13−J13K14−J14BE01,F01,G01,H01.


## 2.3. The Posterior Distribution Using NIP

According to Jeffrey [20], Jeffreys’ prior is defined as
(11)pπm∝Iπm,m=1,2,3,4,where Iπm=−E∂2fy∣πm/∂πm2 is Fisher’s information matrix.It is assumed that parametersπ1,π2,π3,andπ4 follow Jeffreys’ prior while mixing proportions p1,p2,andp3 assume a uniform prior over an interval 0,1. Thus, the joint prior distribution of parameters is given by
(12)δ7ρy∝1π1π2π3π4,π1,π2,π3,π4>0,p1,p2,p3>0,p1+p2+p3≤1.So, the posterior distribution ofπ1,π2,π3,π4,p1,p2,andp3 jointly using the JP is
(13)g2ρ∣y=Q2−1∑u−0n−s∑v=0u∑w=0vn−suuvvwπ1J21−1π2J22−1π3J23−1π4J24−1exp−π1K21exp−π2K22exp−π3K23exp−π4K24p1E02−1p2F02−1p3G02−11−p1−p2−p3H02−1,where J21=s2, J22=s2, J23=s3, J24=s4, K21=nt−st−ut+∑l=1s1y1l, K22=ut−vt+∑l−1s2y2l, K23=vt−wt+∑l=1s3y3l, K24=wt+∑l=1s4y4l, E01=n−s−u+s1+1, F01=u−v+s2+1, G01=v−w+s3+1, H01=w+s4+1, Q2=J21J22J23J24∑u=0n−s∑v=0u∑w=0vn−suuvvwK21−J21K22−J22K23−J23K24−J24BE02,F02,G02,H02.


## 2.4. The Posterior Distribution Using Jeffreys’ Gamma Prior

The joint prior density ofπ1,π2,π3,andπ4 under Jeffreys’ prior is defined earlier in (12).Now, suppose(14)πi∼JGammaαi,βi,i=1,2,3,4,pi∼U0,1.Considering the independence of priors, we get a joint prior as(15)δ8ρy∝π1α1−1−1e−β1π1π2α2−1−1e−β2π2π3α3−1−1e−β3π3π4α4−1−1e−β4π4.So, the joint posterior distribution of parametersπ1,π2,π3,π4,pi using JGamma prior is
(16)g3ρ∣y=Q3−1∑u=0n−s∑v=0u∑w=0vn−suuvvwπ1J31−1π2J32−1π3J33−1π4J34−1exp−π1K31exp−π2K32exp−π3K33exp−π4K34p1E03−1p2F03−1p3G03−11−p1−p2−p3H03−1,where Q3=J31J32J33J34∑u=0n−s∑v=0u∑w=0vn−suuvvwK31−J31K32−J32K33−J33K34−J34BE03,F03,G03,H03, J31=s1+α1−1, J32=s2+α2−1, J33=s3+α3−1, J34=s4+α4−1, K31=K1+k1, K32=K2+k2, K33=K3+k3,K34=K4+k4, E03=n−s−u+s1+1, F03=u−v+s2+1, G03=v−w+s3+1, H03=w+s4+1.


## 2.5. Bayes Estimators and Posterior Risks Using IP, JP, and JG under SELF and LINEX

### 2.5.1. Loss Functions

Letd^ is the Bayes estimator then ELπ,d^ is its posterior risk. Our purpose, in this study, is to check out the properties of derived estimators and look for efficient loss functions using different priors. Two different loss functions, namely, SELF and LINEX, are used to obtain the Bayes estimators and their posterior risks. The SELF is defined as Ld,π=d−π2. The LINEX loss function can be defined as Lπ^,π=expcπ^−π−cπ^−π−1,c≠0.One can get a Bayes estimator with associated posterior risk under SELF asd^=Eπ and τd^=Eπ2−Eπ2. Similarly, using LINEX, loss Bayes estimators and posterior risks can be obtained by π^=−1/clogEposte−cπ and Riskπ^LINEX=lnEcπ+cEπ. The Bayes estimators and posterior risks under IP, JP, and JG for parameters π1,π2,π3,π4,p1,p2, and p3 under SELF and LINEX are obtained as
(17)π^h1=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1+1Jh2Jh3Jh4Kh1−Jh1+1Kh2−Jh2Kh3−Jh3Kh4−Jh4BE0h,F0h,G0h,H0h,(18)π^h2=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2+1Jh3Jh4Kh1−Jh1Kh2−Jh2+1Kh3−Jh3Kh4−Jh4BE0h,F0h,G0h,H0h,(19)π^h3=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2Jh3+1Jh4Kh1−Jh1Kh2−Jh2Kh3−Jh3+1Kh4−Jh4BE0h,F0h,G0h,H0h,(20)π^h4=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2Jh3Jh4+1Kh1−Jh1Kh2−Jh2Kh3−Jh3Kh4−Jh4+1BE0h,F0h,G0h,H0h,(21)p^h1=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2Jh3Jh4Kh1−Jh1Kh2−Jh2Kh3−Jh3Kh4−Jh4BE0h+1,F0h,G0h,H0h,(22)p^h2=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2Jh3Jh4Kh1−Jh1Kh2−Jh2Kh3−Jh3Kh4−Jh4BE0h,F0h+1,G0h,H0h,(23)p^h3=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2Jh3Jh4Kh1−Jh1Kh2−Jh2Kh3−Jh3Kh4−Jh4BE0h,F0h,G0h+1,H0h,(24)τhπ^h1=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1+2Jh2Jh3Jh4Kh1−Jh1+2Kh2−Jh2Kh3−Jh3Kh4−Jh4BE0h,F0h,G0h,H0h−π^h12,(25)τhπ^h2=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2+2Jh3Jh4Kh1−Jh1Kh2−Jh2+2Kh3−Jh3Kh4−Jh4BE0h,F0h,G0h,H0h−π^h22,(26)τhπ^h3=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2Jh3+2Jh4Kh1−Jh1Kh2−Jh2Kh3−Jh3+2Kh4−Jh4BE0h,F0h,G0h,H0h−π^h32,(27)τhπ^h4=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2Jh3Jh4+2Kh1−Jh1Kh2−Jh2Kh3−Jh3Kh4−Jh4+2BE0h,F0h,G0h,H0h−π^h42,(28)τhp^h1=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2Jh3Jh4Kh1−Jh1Kh2−Jh2Kh3−Jh3Kh4−Jh4BE0h+2,F0h,G0h,H0h−p^h12,(29)τhp^h2=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2Jh3Jh4Kh1−Jh1Kh2−Jh2Kh3−Jh3Kh4−Jh4BE0h,F0h+2,G0h,H0h−p^h22,(30)τhp^h3=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2Jh3Jh4Kh1−Jh1Kh2−Jh2Kh3−Jh3Kh4−Jh4BE0h,F0h,G0h+2,H0h−p^h32,where h=1 for the IP, h=2 for the JP, and h=3 for the JG. The Bayes estimators and posterior risks using IP, JP, and JG prior under LINEX are also derived and are presented in Appendix A.


## 2.5.1. Loss Functions

Letd^ is the Bayes estimator then ELπ,d^ is its posterior risk. Our purpose, in this study, is to check out the properties of derived estimators and look for efficient loss functions using different priors. Two different loss functions, namely, SELF and LINEX, are used to obtain the Bayes estimators and their posterior risks. The SELF is defined as Ld,π=d−π2. The LINEX loss function can be defined as Lπ^,π=expcπ^−π−cπ^−π−1,c≠0.One can get a Bayes estimator with associated posterior risk under SELF asd^=Eπ and τd^=Eπ2−Eπ2. Similarly, using LINEX, loss Bayes estimators and posterior risks can be obtained by π^=−1/clogEposte−cπ and Riskπ^LINEX=lnEcπ+cEπ. The Bayes estimators and posterior risks under IP, JP, and JG for parameters π1,π2,π3,π4,p1,p2, and p3 under SELF and LINEX are obtained as
(17)π^h1=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1+1Jh2Jh3Jh4Kh1−Jh1+1Kh2−Jh2Kh3−Jh3Kh4−Jh4BE0h,F0h,G0h,H0h,(18)π^h2=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2+1Jh3Jh4Kh1−Jh1Kh2−Jh2+1Kh3−Jh3Kh4−Jh4BE0h,F0h,G0h,H0h,(19)π^h3=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2Jh3+1Jh4Kh1−Jh1Kh2−Jh2Kh3−Jh3+1Kh4−Jh4BE0h,F0h,G0h,H0h,(20)π^h4=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2Jh3Jh4+1Kh1−Jh1Kh2−Jh2Kh3−Jh3Kh4−Jh4+1BE0h,F0h,G0h,H0h,(21)p^h1=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2Jh3Jh4Kh1−Jh1Kh2−Jh2Kh3−Jh3Kh4−Jh4BE0h+1,F0h,G0h,H0h,(22)p^h2=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2Jh3Jh4Kh1−Jh1Kh2−Jh2Kh3−Jh3Kh4−Jh4BE0h,F0h+1,G0h,H0h,(23)p^h3=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2Jh3Jh4Kh1−Jh1Kh2−Jh2Kh3−Jh3Kh4−Jh4BE0h,F0h,G0h+1,H0h,(24)τhπ^h1=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1+2Jh2Jh3Jh4Kh1−Jh1+2Kh2−Jh2Kh3−Jh3Kh4−Jh4BE0h,F0h,G0h,H0h−π^h12,(25)τhπ^h2=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2+2Jh3Jh4Kh1−Jh1Kh2−Jh2+2Kh3−Jh3Kh4−Jh4BE0h,F0h,G0h,H0h−π^h22,(26)τhπ^h3=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2Jh3+2Jh4Kh1−Jh1Kh2−Jh2Kh3−Jh3+2Kh4−Jh4BE0h,F0h,G0h,H0h−π^h32,(27)τhπ^h4=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2Jh3Jh4+2Kh1−Jh1Kh2−Jh2Kh3−Jh3Kh4−Jh4+2BE0h,F0h,G0h,H0h−π^h42,(28)τhp^h1=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2Jh3Jh4Kh1−Jh1Kh2−Jh2Kh3−Jh3Kh4−Jh4BE0h+2,F0h,G0h,H0h−p^h12,(29)τhp^h2=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2Jh3Jh4Kh1−Jh1Kh2−Jh2Kh3−Jh3Kh4−Jh4BE0h,F0h+2,G0h,H0h−p^h22,(30)τhp^h3=Qh−1∑u=0n−s∑v=0u∑w=0vn−suuvvwJh1Jh2Jh3Jh4Kh1−Jh1Kh2−Jh2Kh3−Jh3Kh4−Jh4BE0h,F0h,G0h+2,H0h−p^h32,where h=1 for the IP, h=2 for the JP, and h=3 for the JG. The Bayes estimators and posterior risks using IP, JP, and JG prior under LINEX are also derived and are presented in Appendix A.


## 3. Results and Discussions

### 3.1. Simulation Study

Simulated results are obtained for first, second, third, and fourth component densitiesf1y;π1, f2y;π2, f3y;π3, and f4y;π4 chosen randomly from the sample of sizes p1n,p2n, p3n, and 1−p1−p2−p3n, respectively. Results are averaged out after giving 1000 replications when data is considered to be censored at fixed test termination time T=1.05. Failed items can be classified as a subpopulation 1, 2, 3, and 4 of the 4-component mixture of an exponential distribution. To investigate the behaviour of the estimators, the simulated results for n=100,200,300, when π1,π2,π3,π4,p1,p2,p3=1.5,2.5,1.75,2.5,0.35,0.20,0.15 are provided in Table 1 and for π1,π2,π3,π4,p1,p2,p3=1.75,2.05,1.5,2.5,0.25,0.40,0.10 are given in Table 2. A graphical representation is also illustrated and presented in Figure 1.Table 1
Simulation results of informative prior, Jeffreys’ prior, and Jeffreys' gamma prior under different loss functions whenπ1=1.5,π2=2.5,π3=1.75,π4=2.5,p1=0.35,p2=0.20,p3=0.15,T=1.05.
nParameterEstimateSELFLINEXIPJPJeffreys’ gammaIPJPJeffreys’ gamma100π^11.674 (0.168)1.850 (0.259)1.648 (0.163)1.653 (0.021)1.707 (0.113)1.643 (0.001)π^22.560 (0.461)2.625 (0.714)2.424 (0.436)2.429 (0.054)2.304 (0.312)2.360 (0.004)π^31.824 (0.279)2.019 (0.751)1.686 (0.254)1.742 (0.033)1.737 (0.312)1.673 (0.002)π^42.543 (0.335)2.601 (0.453)2.491 (0.326)2.472 (0.041)2.392 (0.207)2.441 (0.003)p^10.338 (0.002)0.326 (0.0028)0.332 (0.0027)0.332 (0.0013)0.318 (0.009)0.308 (0.035)p^20.204 (0.0019)0.209 (0.0020)0.206 (0.0018)0.203 (0.0004)0.205 (0.003)0.200 (0.009)p^30.152 (0.0017)0.1610 (0.0019)0.158 (0.0016)0.152 (0.0003)0.158 (0.002)0.155 (0.005)200π^11.647 (0.097)1.712 (0.121)1.646 (0.095)1.623 (0.011)1.654 (0.055)1.623 (0.001)π^22.530 (0.285)2.559 (0.365)2.472 (0.279)2.455 (0.034)2.385 (0.169)2.434 (0.003)π^31.791 (0.203)1.875 (0.366)1.709 (0.191)1.755 (0.024)1.700 (0.161)1.697 (0.002)π^42.521 (0.199)2.572 (0.237)2.499 (0.195)2.466 (0.024)2.455 (0.112)2.497 (0.002)p^10.339 (0.001)0.336 (0.0015)0.336 (0.0015)0.336 (0.0011)0.326 (0.009)0.313 (0.0337)p^20.203 (0.001)0.205 (0.0010)0.204 (0.0009)0.203 (0.0003)0.203 (0.002)0.199 (0.0076)p^30.153 (0.000)0.156 (0.0010)0.157 (0.0009)0.152 (0.0002)0.155 (0.001)0.153 (0.0039)300π^11.616 (0.065)1.643 (0.078)1.618 (0.066)1.582 (0.008)1.621 (0.037)1.609 (0.0007)π^22.503 (0.205)2.505 (0.244)2.471 (0.203)2.485 (0.025)2.399 (0.118)2.438 (0.0022)π^31.795 (0.163)1.821 (0.248)1.713 (0.154)1.747 (0.020)1.707 (0.114)1.709 (0.0017)π^42.519 (0.142)2.563 (0.162)2.496 (0.140)2.471 (0.017)2.450 (0.077)2.467 (0.0015)p^10.341 (0.001)0.338 (0.0011)0.338 (0.0010)0.339 (0.0011)0.328 (0.009)0.315 (0.0337)p^20.203 (0.000)0.204 (0.0006)0.203 (0.0007)0.201 (0.0002)0.202 (0.002)0.1993 (0.0070)p^30.152 (0.000)0.156 (0.0007)0.155 (0.0006)0.152 (0.0001)0.155 (0.001)0.153 (0.0039)Table 2
Simulation results of informative prior, Jeffreys’ prior, and Jeffreys’ gamma prior under different loss functions whenπ1=1.75,π2=2.05,π3=1.5,π4=2.5,p1=0.25,p2=0.40,p3=0.10,T=1.05.
nParameterEstimateSELFLINEXIPJPJeffreys’ gammaIPJPJeffreys’ gamma100π^11.812 (0.261)2.010 (0.402)1.719 (0.240)1.737 (0.030)1.866 (0.187)1.690 (0.007)π^22.257 (0.220)2.287 (0.278)2.240 (0.215)2.214 (0.027)2.157 (0.130)2.217 (0.006)π^31.752 (0.311)1.921 (0”.138”)1.567 (0.277)1.679 (0.037)1.441 (0.399)1.535 (0.008)π^42.553 (0.387)2.611 (0.542)2.490 (0.378)2.452 (0.046)2.408 (0.254)2.398 (0.011)p^10.253 (0.0025)0.246 (0.0023)0.254 (0.0023)0.250 (0.0007)0.241 (0.0045)0.251 (0.0001)p^20.395 (0.0029)0.387 (0.0028)0.382 (0.0027)0.386 (0.0018)0.372 (0.0151)0.381 (0.0002)p^30.097 (0.0011)0.111 (0.0015)0.108 (0.0012)0.098 (0.0001)0.112 (0.0011)0.108 (0.00004)200π^11.785 (0.154)1.951 (0.208)1.758 (0.150)1.757 (0.018)1.842 (0.097)1.741 (0.004)π^22.204 (0.125)2.193 (0.140)2.185 (0.121)2.167 (0.015)2.126 (0.067)2.177 (0.003)π^31.725 (0.243)1.723 (0.520)1.566 (0.221)1.648 (0.029)1.488 (0.218)1.553 (0.006)π^42.551 (0.234)2.588 (0.283)2.503 (0.229)2.489 (0.028)2.454 (0.135)2.434 (0.006)p^10.252 (0.0013)0.247 (0.0012)0.252 (0.0012)0.249 (0.0005)0.242 (0.0038)0.251 (0.00008)p^20.395 (0.0015)0.392 (0.0015)0.389 (0.0014)0.391 (0.0017)0.377 (0.0147)0.388 (0.00021)p^30.098 (0.0006)0.107 (0.0008)0.104 (0.0006)0.099 (0.0001)0.107 (0.0007)0.104 (0.00002)300π^11.792 (0.114)1.861 (0.136)1.750 (0.108)1.766 (0.013)1.803 (0.064)1.743 (0.003)π^22.149 (0.086)2.151 (0.095)2.162 (0.085)2.146 (0.011)2.107 (0.046)2.158 (0.0026)π^31.694 (0.198)1.646 (0.342)1.580 (0.186)1.635 (0.023)1.475 (0.148)1.556 (0.0056)π^42.518 (0.166)2.566 (0.192)2.493 (0.164)2.495 (0.021)2.493 (0.094)2.461 (0.0051)p^10.251 (0.0009)0.247 (0.0009)0.251 (0.0008)0.249 (0.0004)0.244 (0.0037)0.251 (0.00007)p^20.397 (0.0010)0.393 (0.0010)0.391 (0.0010)0.392 (0.0016)0.379 (0.0144)0.391 (0.00020)p^30.099 (0.0004)0.106 (0.0005)0.103 (0.0004)0.099 (0.00008)0.105 (0.0005)0.103 (0.00001)Figure 1
Graphical representation of Bayes estimator and posterior risk of simulated data.From the obtained results, it is concluded that as the sample size is increased, the Bayes estimates converge to their true values and the posterior risks also decrease. From these tables, it is noted that whenn=100, 200, and 300, Bayes estimates for π^1,π^2,π^3,andπ^4 are overestimated under SELF assuming IP, JP, and JG, but for LINEX loss function, all estimates are underestimated and relatively close to their true value. Mixing proportions p^1,p^2,andp^3 are overestimated for some values and underestimated for few values. It is observed that the performance of LINEX loss function assuming Jeffreys’ gamma prior is better because it has less posterior risk when compared with informative and Jeffreys’ prior. From the graphical representation, it is noted that the maximum value from the data lies on the same point which is obtained from table value.


### 3.2. Data Application

The data was collected by the IARC [21] and is available at GLOBOCAN which is about cancer incidences and mortality. The cancers responsible for the highest incidence in both the genders (total=148,041) in Pakistani population includes breast (n=34038, 23%), lip and oral cavity (n=12761, 8.6%), lung (n=6800, 4.6%), non-Hodgkin lymphoma (n=5964, 4%), and colorectum (n=5335, 3.6%), respectively, whereas the cancers responsible for the highest deaths (totaln=101,113) in Pakistani population includes breast (n=16232, 16.1%), lip and oral cavity (n=7266, 7.2%), lung (n=6013, 5.9%), oesophagus (n=4748, 4.7%), and non-Hodgkin lymphoma (n=4374, 4.3%), respectively. This study is aimed at presenting an analysis of the cancer burden in Pakistan by applying it to a 4-component mixture model, consisting of the estimated number of new cancer cases and deaths in 2012 by age groups. Data is classified into 4 components based on age groups as follows:<45 first group, 45-54 second group, 55-64 third group, and >64 fourth group. Necessary calculations thus obtained are as follows:Real dataset for the mixture of exponential model incidences of male:(31)T=1000.25,n1=57,n2=44,n3=43,n4=65,∑i=1n1x1i=8939,∑i=1n2x2i=8846,∑i=1n3x3i=9539,∑i=1n4x4i=14535.Real dataset for the mixture of exponential model incidences of female:(32)T=1000.05,n1=59,n2=45,n3=44,n4=69,∑i=1n1x1i=11901,∑i=1n2x2i=9589,∑i=1n3x3i=9758,∑i=1n4x4i=8146.Real dataset for the mixture of exponential model deaths of male:(33)T=500.25,n1=56,n2=44,n3=40,n4=61,∑i=1n1x1i=4838,∑i=1n2x2i=5831,∑i=1n3x3i=6096,∑i=1n4x4i=11388.Real dataset for the mixture of exponential model deaths of female:(34)T=500.05,n1=56,n2=44,n3=41,n4=68,∑i=1n1x1i=5348,∑i=1n2x2i=5368,∑i=1n3x3i=5981,∑i=1n4x4i=7747.The Bayes estimators and posterior risks using IP, JP, and JG under SELF and LINEX loss functions are presented in Tables3 and 4. The reciprocal values of Bayes estimators are representing the average no. of incidences and deaths by age in the Pakistani male and female population. π^1 refers to the average no. of incidences and deaths in male and female below the age of 44; similarly, π^2,π^3,andπ^4 represent the average no. of incidences and deaths in male and female for the age 45-54, 55-64, and above 65, respectively. And it is also noted that the Bayes estimates under LINEX loss function assuming Jeffreys’ prior are more efficient because their posterior risks are less as compare to IP and JG prior.Table 3
Bayes estimates and posterior risk for incidences of male and female under different priors and loss functions.
ParameterEstimateSELFLINEXIPJPJeffreys’ gammaIPJPJeffreys’ gammaMaleπ^10.0064 (8.28×10−5)0.0062 (8.10×10−5)0.0064 (8.51×10−4)0.0068 (1.03×10−7)0.0060 (1.01×10−7)0.0060 (4.25×10−7)π^20.0051 (8.66×10−5)0.0042 (1.08×10−4)0.0047 (1.22×10−3)0.0045 (1.07×10−7)0.0039 (1.36×10−7)0.0040 (6.12×10−7)π^30.0032 (1.32×10−4)0.0028 (1.04×10−4)0.0042 (1.31×10−3)0.0028 (1.65×10−7)0.0030 (1.31×10−7)0.0035 (6.56×10−7)π^40.0029 (7.05×10−5)0.0032 (7.09×10−5)0.0028 (5.85×10−4)0.0030 (8.94×10−8)0.0035 (8.88×10−8)0.0030 (2.92×10−7)p^10.2555 (0.0008)0.2545 (0.0008)0.2547 (0.0008)0.2534 (0.0004)0.2536 (0.0004)0.2508 (0.0038)p^20.2053 (0.0007)0.2058 (0.0008)0.2041 (0.0009)0.2038 (0.0002)0.2052 (0.0003)0.2019 (0.0021)p^30.2139 (0.0012)0.2235 (0.0012)0.2048 (0.0010)0.2153 (0.0004)0.2227 (0.0004)0.2025 (0.0022)Femaleπ^10.0046 (8.28×10−4)0.0043 (7.51×10−5)0.0038 (1.61×10−4)0.0044 (1.05×10−7)0.0040 (8.60×10−8)0.0034 (8.08×10−7)π^20.0024 (1.17×10−3)0.0034 (1.47×10−4)0.0034 (2.04×10−4)0.0034 (1.09×10−7)0.0031 (2.30×10−7)0.0029 (1.02×10−6)π^30.0041 (1.54×10−3)0.0024 (1.10×10−4)0.0040 (1.75×10−4)0.0043 (1.56×10−7)0.0019 (1.68×10−7)0.0035 (8.78×10−7)π^40.0089 (1.11×10−4)0.0084 (1.06×10−4)0.0089 (1.12×10−4)0.0075 (1.39×10−7)0.0079 (1.26×10−7)0.0082 (5.61×10−7)p^10.2574 (0.0009)0.2595 (0.0009)0.2764 (0.0015)0.2597 (0.0005)0.2492 (0.0004)0.2710 (0.0053)p^20.2476 (0.0013)0.2147 (0.0013)0.2235 (0.0016)0.2542 (0.0005)0.2151 (0.0005)0.2203 (0.0031)p^30.2079 (0.0011)0.2326 (0.0014)0.2068 (0.0012)0.1950 (0.0003)0.2557 (0.0007)0.2203 (0.0025)Table 4
Bayes estimates and posterior risk for deaths of male and female under different priors and loss functions.
ParameterEstimateSELFLINEXIPJPJeffreys’ gammaIPJPJeffreys’ gammaMaleπ^10.0119 (2.72×10−4)0.0112 (2.61×10−3)0.0116 (2.72×10−4)0.0125 (3.40×10−7)0.0119 (3.27×10−7)0.0120 (1.36×10−6)π^20.0072 (1.90×10−4)0.0064 (1.72×10−3)0.0071 (1.93×10−4)0.0066 (2.36×10−7)0.0056 (2.15×10−7)0.0075 (9.68×10−7)π^30.0061 (1.64×10−4)0.0049 (1.43×10−4)0.0062 (1.75×10−4)0.0055 (2.06×10−7)0.0042 (1.80×10−7)0.0056 (8.77×10−7)π^40.0032 (2.41×10−5)0.0032 (3.05×10−4)0.0033 (2.74×10−5)0.0027 (3.01×10−8)0.0025 (3.82×10−8)0.0039 (1.37×10−7)p^10.2521 (0.0008)0.2524 (0.0004)0.2524 (0.0008)0.2500 (0.0004)0.2513 (0.0004)0.2486 (0.0037)p^20.2153 (0.0008)0.2086 (0.0003)0.2069 (0.0007)0.2136 (0.0003)0.2079 (0.0003)0.2047 (0.0022)p^30.1879 (0.0007)0.2002 (0.0003)0.1930 (0.0008)0.1891 (0.0002)0.1995 (0.0003)0.1912 (0.0018)Femaleπ^10.0105 (2.46×10−4)0.0099 (2.34×10−3)0.0102 (2.52×10−3)0.0110 (2.77×10−6)0.0095 (2.63×10−6)0.0112 (1.26×10−6)π^20.0073 (3.78×10−4)0.0066 (2.76×10−3)0.0072 (3.96×10−3)0.0070 (4.45×10−6)0.0060 (3.11×10−6)0.0067 (1.98×10−6)π^30.0032 (1.22×10−4)0.0028 (5.96×10−4)0.0034 (1.52×10−3)0.0037 (1.58×10−6)0.0033 (6.71×10−7)0.0030 (7.62×10−7)π^40.0081 (1.85×10−4)0.0077 (1.63×10−3)0.0081 (1.99×10−3)0.0075 (2.18×10−6)0.0071 (1.83×10−6)0.0089 (9.96×10−7)p^10.2422 (0.0007)0.2433 (0.0007)0.2436 (0.0007)0.2342 (0.0116)0.2353 (0.0118)0.2402 (0.0033)p^20.2131 (0.0011)0.2031 (0.0009)0.2051 (0.0011)0.2070 (0.0086)0.1981 (0.0073)0.2027 (0.0023)p^30.2506 (0.0012)0.2527 (0.0011)0.2498 (0.0014)0.2382 (0.0134)0.2438 (0.0139)0.2457 (0.0040)


## 3.1. Simulation Study

Simulated results are obtained for first, second, third, and fourth component densitiesf1y;π1, f2y;π2, f3y;π3, and f4y;π4 chosen randomly from the sample of sizes p1n,p2n, p3n, and 1−p1−p2−p3n, respectively. Results are averaged out after giving 1000 replications when data is considered to be censored at fixed test termination time T=1.05. Failed items can be classified as a subpopulation 1, 2, 3, and 4 of the 4-component mixture of an exponential distribution. To investigate the behaviour of the estimators, the simulated results for n=100,200,300, when π1,π2,π3,π4,p1,p2,p3=1.5,2.5,1.75,2.5,0.35,0.20,0.15 are provided in Table 1 and for π1,π2,π3,π4,p1,p2,p3=1.75,2.05,1.5,2.5,0.25,0.40,0.10 are given in Table 2. A graphical representation is also illustrated and presented in Figure 1.Table 1
Simulation results of informative prior, Jeffreys’ prior, and Jeffreys' gamma prior under different loss functions whenπ1=1.5,π2=2.5,π3=1.75,π4=2.5,p1=0.35,p2=0.20,p3=0.15,T=1.05.
nParameterEstimateSELFLINEXIPJPJeffreys’ gammaIPJPJeffreys’ gamma100π^11.674 (0.168)1.850 (0.259)1.648 (0.163)1.653 (0.021)1.707 (0.113)1.643 (0.001)π^22.560 (0.461)2.625 (0.714)2.424 (0.436)2.429 (0.054)2.304 (0.312)2.360 (0.004)π^31.824 (0.279)2.019 (0.751)1.686 (0.254)1.742 (0.033)1.737 (0.312)1.673 (0.002)π^42.543 (0.335)2.601 (0.453)2.491 (0.326)2.472 (0.041)2.392 (0.207)2.441 (0.003)p^10.338 (0.002)0.326 (0.0028)0.332 (0.0027)0.332 (0.0013)0.318 (0.009)0.308 (0.035)p^20.204 (0.0019)0.209 (0.0020)0.206 (0.0018)0.203 (0.0004)0.205 (0.003)0.200 (0.009)p^30.152 (0.0017)0.1610 (0.0019)0.158 (0.0016)0.152 (0.0003)0.158 (0.002)0.155 (0.005)200π^11.647 (0.097)1.712 (0.121)1.646 (0.095)1.623 (0.011)1.654 (0.055)1.623 (0.001)π^22.530 (0.285)2.559 (0.365)2.472 (0.279)2.455 (0.034)2.385 (0.169)2.434 (0.003)π^31.791 (0.203)1.875 (0.366)1.709 (0.191)1.755 (0.024)1.700 (0.161)1.697 (0.002)π^42.521 (0.199)2.572 (0.237)2.499 (0.195)2.466 (0.024)2.455 (0.112)2.497 (0.002)p^10.339 (0.001)0.336 (0.0015)0.336 (0.0015)0.336 (0.0011)0.326 (0.009)0.313 (0.0337)p^20.203 (0.001)0.205 (0.0010)0.204 (0.0009)0.203 (0.0003)0.203 (0.002)0.199 (0.0076)p^30.153 (0.000)0.156 (0.0010)0.157 (0.0009)0.152 (0.0002)0.155 (0.001)0.153 (0.0039)300π^11.616 (0.065)1.643 (0.078)1.618 (0.066)1.582 (0.008)1.621 (0.037)1.609 (0.0007)π^22.503 (0.205)2.505 (0.244)2.471 (0.203)2.485 (0.025)2.399 (0.118)2.438 (0.0022)π^31.795 (0.163)1.821 (0.248)1.713 (0.154)1.747 (0.020)1.707 (0.114)1.709 (0.0017)π^42.519 (0.142)2.563 (0.162)2.496 (0.140)2.471 (0.017)2.450 (0.077)2.467 (0.0015)p^10.341 (0.001)0.338 (0.0011)0.338 (0.0010)0.339 (0.0011)0.328 (0.009)0.315 (0.0337)p^20.203 (0.000)0.204 (0.0006)0.203 (0.0007)0.201 (0.0002)0.202 (0.002)0.1993 (0.0070)p^30.152 (0.000)0.156 (0.0007)0.155 (0.0006)0.152 (0.0001)0.155 (0.001)0.153 (0.0039)Table 2
Simulation results of informative prior, Jeffreys’ prior, and Jeffreys’ gamma prior under different loss functions whenπ1=1.75,π2=2.05,π3=1.5,π4=2.5,p1=0.25,p2=0.40,p3=0.10,T=1.05.
nParameterEstimateSELFLINEXIPJPJeffreys’ gammaIPJPJeffreys’ gamma100π^11.812 (0.261)2.010 (0.402)1.719 (0.240)1.737 (0.030)1.866 (0.187)1.690 (0.007)π^22.257 (0.220)2.287 (0.278)2.240 (0.215)2.214 (0.027)2.157 (0.130)2.217 (0.006)π^31.752 (0.311)1.921 (0”.138”)1.567 (0.277)1.679 (0.037)1.441 (0.399)1.535 (0.008)π^42.553 (0.387)2.611 (0.542)2.490 (0.378)2.452 (0.046)2.408 (0.254)2.398 (0.011)p^10.253 (0.0025)0.246 (0.0023)0.254 (0.0023)0.250 (0.0007)0.241 (0.0045)0.251 (0.0001)p^20.395 (0.0029)0.387 (0.0028)0.382 (0.0027)0.386 (0.0018)0.372 (0.0151)0.381 (0.0002)p^30.097 (0.0011)0.111 (0.0015)0.108 (0.0012)0.098 (0.0001)0.112 (0.0011)0.108 (0.00004)200π^11.785 (0.154)1.951 (0.208)1.758 (0.150)1.757 (0.018)1.842 (0.097)1.741 (0.004)π^22.204 (0.125)2.193 (0.140)2.185 (0.121)2.167 (0.015)2.126 (0.067)2.177 (0.003)π^31.725 (0.243)1.723 (0.520)1.566 (0.221)1.648 (0.029)1.488 (0.218)1.553 (0.006)π^42.551 (0.234)2.588 (0.283)2.503 (0.229)2.489 (0.028)2.454 (0.135)2.434 (0.006)p^10.252 (0.0013)0.247 (0.0012)0.252 (0.0012)0.249 (0.0005)0.242 (0.0038)0.251 (0.00008)p^20.395 (0.0015)0.392 (0.0015)0.389 (0.0014)0.391 (0.0017)0.377 (0.0147)0.388 (0.00021)p^30.098 (0.0006)0.107 (0.0008)0.104 (0.0006)0.099 (0.0001)0.107 (0.0007)0.104 (0.00002)300π^11.792 (0.114)1.861 (0.136)1.750 (0.108)1.766 (0.013)1.803 (0.064)1.743 (0.003)π^22.149 (0.086)2.151 (0.095)2.162 (0.085)2.146 (0.011)2.107 (0.046)2.158 (0.0026)π^31.694 (0.198)1.646 (0.342)1.580 (0.186)1.635 (0.023)1.475 (0.148)1.556 (0.0056)π^42.518 (0.166)2.566 (0.192)2.493 (0.164)2.495 (0.021)2.493 (0.094)2.461 (0.0051)p^10.251 (0.0009)0.247 (0.0009)0.251 (0.0008)0.249 (0.0004)0.244 (0.0037)0.251 (0.00007)p^20.397 (0.0010)0.393 (0.0010)0.391 (0.0010)0.392 (0.0016)0.379 (0.0144)0.391 (0.00020)p^30.099 (0.0004)0.106 (0.0005)0.103 (0.0004)0.099 (0.00008)0.105 (0.0005)0.103 (0.00001)Figure 1
Graphical representation of Bayes estimator and posterior risk of simulated data.From the obtained results, it is concluded that as the sample size is increased, the Bayes estimates converge to their true values and the posterior risks also decrease. From these tables, it is noted that whenn=100, 200, and 300, Bayes estimates for π^1,π^2,π^3,andπ^4 are overestimated under SELF assuming IP, JP, and JG, but for LINEX loss function, all estimates are underestimated and relatively close to their true value. Mixing proportions p^1,p^2,andp^3 are overestimated for some values and underestimated for few values. It is observed that the performance of LINEX loss function assuming Jeffreys’ gamma prior is better because it has less posterior risk when compared with informative and Jeffreys’ prior. From the graphical representation, it is noted that the maximum value from the data lies on the same point which is obtained from table value.


## 3.2. Data Application

The data was collected by the IARC [21] and is available at GLOBOCAN which is about cancer incidences and mortality. The cancers responsible for the highest incidence in both the genders (total=148,041) in Pakistani population includes breast (n=34038, 23%), lip and oral cavity (n=12761, 8.6%), lung (n=6800, 4.6%), non-Hodgkin lymphoma (n=5964, 4%), and colorectum (n=5335, 3.6%), respectively, whereas the cancers responsible for the highest deaths (totaln=101,113) in Pakistani population includes breast (n=16232, 16.1%), lip and oral cavity (n=7266, 7.2%), lung (n=6013, 5.9%), oesophagus (n=4748, 4.7%), and non-Hodgkin lymphoma (n=4374, 4.3%), respectively. This study is aimed at presenting an analysis of the cancer burden in Pakistan by applying it to a 4-component mixture model, consisting of the estimated number of new cancer cases and deaths in 2012 by age groups. Data is classified into 4 components based on age groups as follows:<45 first group, 45-54 second group, 55-64 third group, and >64 fourth group. Necessary calculations thus obtained are as follows:Real dataset for the mixture of exponential model incidences of male:(31)T=1000.25,n1=57,n2=44,n3=43,n4=65,∑i=1n1x1i=8939,∑i=1n2x2i=8846,∑i=1n3x3i=9539,∑i=1n4x4i=14535.Real dataset for the mixture of exponential model incidences of female:(32)T=1000.05,n1=59,n2=45,n3=44,n4=69,∑i=1n1x1i=11901,∑i=1n2x2i=9589,∑i=1n3x3i=9758,∑i=1n4x4i=8146.Real dataset for the mixture of exponential model deaths of male:(33)T=500.25,n1=56,n2=44,n3=40,n4=61,∑i=1n1x1i=4838,∑i=1n2x2i=5831,∑i=1n3x3i=6096,∑i=1n4x4i=11388.Real dataset for the mixture of exponential model deaths of female:(34)T=500.05,n1=56,n2=44,n3=41,n4=68,∑i=1n1x1i=5348,∑i=1n2x2i=5368,∑i=1n3x3i=5981,∑i=1n4x4i=7747.The Bayes estimators and posterior risks using IP, JP, and JG under SELF and LINEX loss functions are presented in Tables3 and 4. The reciprocal values of Bayes estimators are representing the average no. of incidences and deaths by age in the Pakistani male and female population. π^1 refers to the average no. of incidences and deaths in male and female below the age of 44; similarly, π^2,π^3,andπ^4 represent the average no. of incidences and deaths in male and female for the age 45-54, 55-64, and above 65, respectively. And it is also noted that the Bayes estimates under LINEX loss function assuming Jeffreys’ prior are more efficient because their posterior risks are less as compare to IP and JG prior.Table 3
Bayes estimates and posterior risk for incidences of male and female under different priors and loss functions.
ParameterEstimateSELFLINEXIPJPJeffreys’ gammaIPJPJeffreys’ gammaMaleπ^10.0064 (8.28×10−5)0.0062 (8.10×10−5)0.0064 (8.51×10−4)0.0068 (1.03×10−7)0.0060 (1.01×10−7)0.0060 (4.25×10−7)π^20.0051 (8.66×10−5)0.0042 (1.08×10−4)0.0047 (1.22×10−3)0.0045 (1.07×10−7)0.0039 (1.36×10−7)0.0040 (6.12×10−7)π^30.0032 (1.32×10−4)0.0028 (1.04×10−4)0.0042 (1.31×10−3)0.0028 (1.65×10−7)0.0030 (1.31×10−7)0.0035 (6.56×10−7)π^40.0029 (7.05×10−5)0.0032 (7.09×10−5)0.0028 (5.85×10−4)0.0030 (8.94×10−8)0.0035 (8.88×10−8)0.0030 (2.92×10−7)p^10.2555 (0.0008)0.2545 (0.0008)0.2547 (0.0008)0.2534 (0.0004)0.2536 (0.0004)0.2508 (0.0038)p^20.2053 (0.0007)0.2058 (0.0008)0.2041 (0.0009)0.2038 (0.0002)0.2052 (0.0003)0.2019 (0.0021)p^30.2139 (0.0012)0.2235 (0.0012)0.2048 (0.0010)0.2153 (0.0004)0.2227 (0.0004)0.2025 (0.0022)Femaleπ^10.0046 (8.28×10−4)0.0043 (7.51×10−5)0.0038 (1.61×10−4)0.0044 (1.05×10−7)0.0040 (8.60×10−8)0.0034 (8.08×10−7)π^20.0024 (1.17×10−3)0.0034 (1.47×10−4)0.0034 (2.04×10−4)0.0034 (1.09×10−7)0.0031 (2.30×10−7)0.0029 (1.02×10−6)π^30.0041 (1.54×10−3)0.0024 (1.10×10−4)0.0040 (1.75×10−4)0.0043 (1.56×10−7)0.0019 (1.68×10−7)0.0035 (8.78×10−7)π^40.0089 (1.11×10−4)0.0084 (1.06×10−4)0.0089 (1.12×10−4)0.0075 (1.39×10−7)0.0079 (1.26×10−7)0.0082 (5.61×10−7)p^10.2574 (0.0009)0.2595 (0.0009)0.2764 (0.0015)0.2597 (0.0005)0.2492 (0.0004)0.2710 (0.0053)p^20.2476 (0.0013)0.2147 (0.0013)0.2235 (0.0016)0.2542 (0.0005)0.2151 (0.0005)0.2203 (0.0031)p^30.2079 (0.0011)0.2326 (0.0014)0.2068 (0.0012)0.1950 (0.0003)0.2557 (0.0007)0.2203 (0.0025)Table 4
Bayes estimates and posterior risk for deaths of male and female under different priors and loss functions.
ParameterEstimateSELFLINEXIPJPJeffreys’ gammaIPJPJeffreys’ gammaMaleπ^10.0119 (2.72×10−4)0.0112 (2.61×10−3)0.0116 (2.72×10−4)0.0125 (3.40×10−7)0.0119 (3.27×10−7)0.0120 (1.36×10−6)π^20.0072 (1.90×10−4)0.0064 (1.72×10−3)0.0071 (1.93×10−4)0.0066 (2.36×10−7)0.0056 (2.15×10−7)0.0075 (9.68×10−7)π^30.0061 (1.64×10−4)0.0049 (1.43×10−4)0.0062 (1.75×10−4)0.0055 (2.06×10−7)0.0042 (1.80×10−7)0.0056 (8.77×10−7)π^40.0032 (2.41×10−5)0.0032 (3.05×10−4)0.0033 (2.74×10−5)0.0027 (3.01×10−8)0.0025 (3.82×10−8)0.0039 (1.37×10−7)p^10.2521 (0.0008)0.2524 (0.0004)0.2524 (0.0008)0.2500 (0.0004)0.2513 (0.0004)0.2486 (0.0037)p^20.2153 (0.0008)0.2086 (0.0003)0.2069 (0.0007)0.2136 (0.0003)0.2079 (0.0003)0.2047 (0.0022)p^30.1879 (0.0007)0.2002 (0.0003)0.1930 (0.0008)0.1891 (0.0002)0.1995 (0.0003)0.1912 (0.0018)Femaleπ^10.0105 (2.46×10−4)0.0099 (2.34×10−3)0.0102 (2.52×10−3)0.0110 (2.77×10−6)0.0095 (2.63×10−6)0.0112 (1.26×10−6)π^20.0073 (3.78×10−4)0.0066 (2.76×10−3)0.0072 (3.96×10−3)0.0070 (4.45×10−6)0.0060 (3.11×10−6)0.0067 (1.98×10−6)π^30.0032 (1.22×10−4)0.0028 (5.96×10−4)0.0034 (1.52×10−3)0.0037 (1.58×10−6)0.0033 (6.71×10−7)0.0030 (7.62×10−7)π^40.0081 (1.85×10−4)0.0077 (1.63×10−3)0.0081 (1.99×10−3)0.0075 (2.18×10−6)0.0071 (1.83×10−6)0.0089 (9.96×10−7)p^10.2422 (0.0007)0.2433 (0.0007)0.2436 (0.0007)0.2342 (0.0116)0.2353 (0.0118)0.2402 (0.0033)p^20.2131 (0.0011)0.2031 (0.0009)0.2051 (0.0011)0.2070 (0.0086)0.1981 (0.0073)0.2027 (0.0023)p^30.2506 (0.0012)0.2527 (0.0011)0.2498 (0.0014)0.2382 (0.0134)0.2438 (0.0139)0.2457 (0.0040)


## 4. Conclusion

This study is aimed at developing a 4-component mixture model of exponential distribution using type I censoring under SELF and LINEX loss function and IP, JP, and JG priors. The motivation of this study is to show the application of the exponential mixture model to cancer data under the Bayesian paradigm. It is suggested that mixture models can ideally be applied to analyze cancer data. Bayes estimates are found overestimated for some values and underestimated for few values. In a simulation study under SELF, it is noted that Jeffreys’ gamma prior is best because their posterior risks are less as compared to IP and Jeffreys’ prior. In LINEX loss function, Jeffreys’ gamma prior can be preferred as compared to IP and Jeffreys’ prior at censoring timeT=1.05. The application of 4 components of exponential mixture distribution is presented using cancer data in which incidences and deaths of the male and female population of Pakistan are studied. The values of Bayes estimates (reciprocals) are representing the average no. of new cases by age in the Pakistani male and female population. π^1 represents the average no. of incidences in male and female below the age of 44; similarly, π^2,π^3,andπ^4 represent the average no. of incidences in male and female from the age 45-54, 55-64, and above 65, respectively. And it is also noted that the Bayes estimates under LINEX loss function assuming Jeffreys’ prior is more efficient because their posterior risks are less as compare to IP and JG prior.For the case of the number of deaths,π^1 represents Bayes estimates and the reciprocal of it represents average no. of death in male and female below the age of 44; similarly, the average no. of deaths in male and female from the age 45-54, 55-64, and above 65 are represented by reciprocal of π^2,π^3,andπ^4, respectively. The best loss function is found to be the LINEX loss function assuming Jeffreys’ prior for the male population. In the case of the female population, the best loss function is SELF assuming Jeffreys’ prior.



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*Source: 6289337-2021-10-12.xml*