# A Family of Bayesian Estimators for the Two-Parametric Burr Type II Distribution

Authors: R. Alshenawy; Navid Feroze; Ali Al-Alwan; Mahreen Saleem; Sahidul Islam
Journal: Journal of Function Spaces
 (2022)

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## Abstract

This study discusses the posterior estimation for the parameters of the Burr type II distribution (BIID). The informative and noninformative priors along with different loss functions have also been assumed for the posterior estimation. The applicability of the proposed distribution has also been discussed. The modeling capability of the proposed model has been compared with seven classes of the lifetime distributions using real data. The generalizations of Weibull, exponential, Rayleigh, gamma, log normal, Pareto, Maxwell, Levy, Laplace, inverse gamma, Gompertz, chi-square, inverse chi-square, half normal, and log-logistic distributions have been considered for the comparison. The comparison has been made based on different goodness-of-fit criteria, such as Akaike information criteria (AIC), Bayesian information criteria (BIC), and Kolmogorov-Smirnov (KS) test. Based on the results from the study, it can be suggested that the BIID can efficiently replace commonly used lifetime distributions and their modifications. The results under this model were comparable with different conventional/modified distributions having up to six parameters.


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## Body

## 1. Introduction

Lifetime distributions are very useful in reliability analysis. There are many conventional models available in literature to model life data. A class of life distribution including twelve models was introduced by Burr [1]. From this family of distribution, the Burr type III (BIIID), Burr type X (BXD), and Burr type XII (BXIID) have been frequently used for lifetime analysis. However, other members of the family have not been considered much for estimating the lifetimes. Similarly, BIID has not received much attention in modeling life datasets. The analysis of such deprived distribution is always of interest for research for exploring their hidden properties and applications.The contributions regarding the Burr family of distributions can be seen from the following works. Abd-Elfattah and Alharbey [2] used the trimmed samples to estimate the parameters of Burr type III distribution (BIIID). The comparison between Bayesian approach and maximum likelihood (ML) approach was considered. Azimi and Yaghmaei [3] estimated the reliability function of the BIIID using doubly censored samples. The Bayes estimates were obtained using various loss functions and priors. Pant and Hedrick [4] derived the BIIID family of distribution in the context of univariate L correlations and the L moments. Altindag et al. [5] used ML estimation via EM algorithm to analyze the parameters of BIIID using type II censored samples.Recently, Feroze and Aslam [6] obtained the ML estimates for the parameters of the Burr type V distribution (BVD) under left censored samples. Feroze and Aslam [7] considered the Bayes point and interval estimators for the parameters of the BVD. The contributions regarding BXD can be seen from the following. Surles and Padgett [8] studied the applications and analysis the BXD. Raqab and Kundu [9] suggested that the two parametric BXD can be used to model the skewed datasets efficiently as compared to the Weibull and gamma distributions. Moussa [10] considered the point and interval estimation of the BXD using the classical and Bayesian methods. Aludaat et al. [11] obtained Bayesian and classical estimators under grouped data for the parameter of the BXD. Feroze and Aslam [12] analyzed the BXD using Bayesian methods. Ahmad et al. [13] obtained Bayes estimates and maximum likelihood estimates for the BXD based on doubly type II censored.The analysis of BXIID in different situations has been quite frequent. Following contributions present the glimpses regarding the use of BXIID. Moussa and Jaheen [14] obtained the Bayes estimation for reliability function of BXIID. Soliman [15] suggested the ML and Bayes estimators for estimation of reliability from BXIID. Shao et al. [16] used BXIID to analyze the flood frequency. Yarmohammadi and Pazira [17] worked on the classical estimators for the BXIID such as Minimum Mean Squared Error (min MSE), the ML and the minimal estimators. Rastogi and Tripathi [18] considered the problem of estimating reliability function of BXIID on the basis of a censored type II sample. The comparison between Bayes estimation and ML estimates was made. Tahir et al. [19] discussed the estimation of mixture of BXIID under type I censored dataset. Amein and Sayed-Ahmed [20] introduced extended BXIID and estimated its parameters using type I hybrid censoring. Xin et al. [21] considered Bayes estimators and ML estimators of the parameters of three parametric exponentiated BXIID. Rabies and Li [22] investigated the behavior of reliability function from the BXIID using empirical Bayes methods.However, the BIID has not yet received the desired attention of the researchers. Feroze et al. [23] addressed the problem of estimating parameters of the BIID on the basis of the ML estimates when the samples were left censored. Sindhu et al. [24] addressed the problem of estimating the Burr type II distribution using trimmed samples. However, the said contributions have considered the Bayesian analysis of a single parameter from the BIID. We have attempted to analyze the two-parametric BIID using Bayesian methods. Different priors and loss functions have been assumed for the estimation of the model parameters. Since the Bayes estimators in closed form were unavailable, the numerical integrations were employed to obtain the said estimates numerically.


## 2. Materials and Methods

The probability density function for BIID is(1)fx∣λ,γ=γλe−x/λ1+e−x/λ−γ+1,−∞<x<∞,λ,γ>0,where the shape parameter is γ≥0.5 and scale parameter is λ>0.Cumulative distribution functions (CDF) for BIID are(2)Fx,λ,γ=1+e−x/λγ.In this study, the Bayesian analysis of the BIID has been considered. The numerical integration has been used to obtain the Bayes estimates. Further, different loss functions have been used for the posterior estimation. A real dataset has been used to illustrate the application of the proposed model. The suitably proposed model has been investigated by comparing it with different classes of lifetime distributions such as exponentiated distribution (ED), Beta Extended Generalized Distribution (BEGD), Weibull Extended Generalized Distribution (WEGD), Exponentiated Generalized G Distribution (EGGD), Gamma G-I Distribution (GGID), Gamma G-II Distribution (GGIID), and Life Time Distribution (LTD).


### 2.1. Loss Function

The following four loss functions have been used for obtaining Bayes estimates of the model parameters. The loss functions are used to estimate the model parameters and associated posterior risks. The loss functions can be either symmetric or asymmetric. The symmetric loss function gives equal weights to over- or underestimation. On the other hand, the asymmetric loss function considers different weights for over- and underestimation.


#### 2.1.1. Squared Error Loss Function (SELF)

SELF is defined as:(3)Lψ,ψSELF=ψ−ψSELF2,where ψ=λ,γ.The SELF under the Bayes estimator is(4)ψSELF=Eψ.The posterior risk under SELF is(5)RSELF=Eψ−ψSELF2=Eψ2−Eψ2.


#### 2.1.2. Quadratic Loss Function (QLF)

The QLF is define as(6)Lψ,ψQLF=ψ−ψQLFψ2.These are the estimators related to the Bayes under QLF which is given as(7)BQLF=Eψ−1Eψ−2−1.The risk under QLF using is(8)RQLF=Eψ−ψQLFψ−2=1−Eψ−12Eψ−2.


#### 2.1.3. Precautionary Loss Function (PLF)

The PLF can be presented as(9)LψPLF,ψ=ψPLF−ψ2ψPLF.The Bayes estimator for PLF is(10)ψPLF=Eψ21/2.Under PLF, the Bayes risk is(11)RPLF=2ψPLF−Eψ.


#### 2.1.4. Entropy Loss Function (ELF)

The ELF is defined as(12)Lψ,ψELF=ψELFψ−lnψELFψ.Under the Bayes estimator is written as(13)ψELF=Eψ−1−1.The risk under ELF can be written as(14)RELF=ELψELF,ψ=Elnψ−lnψELF.


### 2.2. Bayesian Estimation of the Burr Type II Distribution

The main advantage of the Bayesian methods to the classical methods is that these methods allow us to incorporate the prior information to analyze the model parameters. In addition, the Bayesian methods also provide more efficient results as compared to classical methods, especially in the case of the small samples. The Bayesian estimation of the BIID has been given in the following.For a sample of size “n,” the likelihood function for the BIID is
(15)Lx;λ,γ∝λ−nγn∏i=1nexp−xiλ1+exp−xiλ−γ−1.The noninformative prior forψ=λ,γ is given as
(16)g1ψ∝1.The conjugate gamma prior forψ=λ,γ is
(17)g2ψ∝ψa−1exp−bψ.Using (15) and (16), the posterior distribution under noninformative prior is
(18)κ1λ,γx∝λ−nγn∏i=1nexp−xiλ1+exp−xiλ−γ−1.Similarly, using (15) and (17), the posterior distribution under the informative prior is
(19)κ2λ,γx∝λ−n+a1−1γn+a2−1exp−b1λexp−b2γ∏i=1nexp−xiλ1+exp−xiλ−γ−1.From (18) and (19), it can be assessed that the Bayes estimators under proposed loss functions cannot be obtained explicitly. Hence, the numerical integrations have been used for approximate solutions of the proposed estimates.


## 2.1. Loss Function

The following four loss functions have been used for obtaining Bayes estimates of the model parameters. The loss functions are used to estimate the model parameters and associated posterior risks. The loss functions can be either symmetric or asymmetric. The symmetric loss function gives equal weights to over- or underestimation. On the other hand, the asymmetric loss function considers different weights for over- and underestimation.


### 2.1.1. Squared Error Loss Function (SELF)

SELF is defined as:(3)Lψ,ψSELF=ψ−ψSELF2,where ψ=λ,γ.The SELF under the Bayes estimator is(4)ψSELF=Eψ.The posterior risk under SELF is(5)RSELF=Eψ−ψSELF2=Eψ2−Eψ2.


### 2.1.2. Quadratic Loss Function (QLF)

The QLF is define as(6)Lψ,ψQLF=ψ−ψQLFψ2.These are the estimators related to the Bayes under QLF which is given as(7)BQLF=Eψ−1Eψ−2−1.The risk under QLF using is(8)RQLF=Eψ−ψQLFψ−2=1−Eψ−12Eψ−2.


### 2.1.3. Precautionary Loss Function (PLF)

The PLF can be presented as(9)LψPLF,ψ=ψPLF−ψ2ψPLF.The Bayes estimator for PLF is(10)ψPLF=Eψ21/2.Under PLF, the Bayes risk is(11)RPLF=2ψPLF−Eψ.


### 2.1.4. Entropy Loss Function (ELF)

The ELF is defined as(12)Lψ,ψELF=ψELFψ−lnψELFψ.Under the Bayes estimator is written as(13)ψELF=Eψ−1−1.The risk under ELF can be written as(14)RELF=ELψELF,ψ=Elnψ−lnψELF.


## 2.1.1. Squared Error Loss Function (SELF)

SELF is defined as:(3)Lψ,ψSELF=ψ−ψSELF2,where ψ=λ,γ.The SELF under the Bayes estimator is(4)ψSELF=Eψ.The posterior risk under SELF is(5)RSELF=Eψ−ψSELF2=Eψ2−Eψ2.


## 2.1.2. Quadratic Loss Function (QLF)

The QLF is define as(6)Lψ,ψQLF=ψ−ψQLFψ2.These are the estimators related to the Bayes under QLF which is given as(7)BQLF=Eψ−1Eψ−2−1.The risk under QLF using is(8)RQLF=Eψ−ψQLFψ−2=1−Eψ−12Eψ−2.


## 2.1.3. Precautionary Loss Function (PLF)

The PLF can be presented as(9)LψPLF,ψ=ψPLF−ψ2ψPLF.The Bayes estimator for PLF is(10)ψPLF=Eψ21/2.Under PLF, the Bayes risk is(11)RPLF=2ψPLF−Eψ.


## 2.1.4. Entropy Loss Function (ELF)

The ELF is defined as(12)Lψ,ψELF=ψELFψ−lnψELFψ.Under the Bayes estimator is written as(13)ψELF=Eψ−1−1.The risk under ELF can be written as(14)RELF=ELψELF,ψ=Elnψ−lnψELF.


## 2.2. Bayesian Estimation of the Burr Type II Distribution

The main advantage of the Bayesian methods to the classical methods is that these methods allow us to incorporate the prior information to analyze the model parameters. In addition, the Bayesian methods also provide more efficient results as compared to classical methods, especially in the case of the small samples. The Bayesian estimation of the BIID has been given in the following.For a sample of size “n,” the likelihood function for the BIID is
(15)Lx;λ,γ∝λ−nγn∏i=1nexp−xiλ1+exp−xiλ−γ−1.The noninformative prior forψ=λ,γ is given as
(16)g1ψ∝1.The conjugate gamma prior forψ=λ,γ is
(17)g2ψ∝ψa−1exp−bψ.Using (15) and (16), the posterior distribution under noninformative prior is
(18)κ1λ,γx∝λ−nγn∏i=1nexp−xiλ1+exp−xiλ−γ−1.Similarly, using (15) and (17), the posterior distribution under the informative prior is
(19)κ2λ,γx∝λ−n+a1−1γn+a2−1exp−b1λexp−b2γ∏i=1nexp−xiλ1+exp−xiλ−γ−1.From (18) and (19), it can be assessed that the Bayes estimators under proposed loss functions cannot be obtained explicitly. Hence, the numerical integrations have been used for approximate solutions of the proposed estimates.


## 3. Results and Discussions

This section starts with the simulation study for the proposed Bayes estimates using different sample sizes, different parametric values, different priors, and different loss functions. The following true parametric values have been used for generation of the simulated samples:λ,γ={0.1,0.1,0.1,0.5,0.5,0.1. After verifying the necessary characteristics (such as convergence and consistency) of the Bayes estimates from the proposed model, the modeling capability of the proposed model has been compared with different classes of the life distributions. These classes include class of LTD, BEGD, WEGD, ED, EKGD, GGID, GGIID, and EGD. The Mathematica and R software have been used for the numerical calculations. Both the parameters of the proposed distribution have been estimated jointly. In the tables, the values given in standard fonts represent the Bayes estimates, whereas the values given in bold fonts represent the amounts of associated posterior risks.The results from the simulation study have been presented in Tables1–11. In the tables, the amounts of posterior risks (PRs) have been presented in bold fonts. From the results, it can be assessed that the estimated values of the model parameters are converging to the true values by increasing the sample size. In addition, the amounts of PRs are decreasing with increase in sample size, which shows that the proposed estimators are consistent in nature. Further, the larger values of λ result in improved estimation for γ. On the other hand, the smaller values of γ result in improved estimation for λ. In comparison of the prior, it can be observed that the estimation under the informative prior is better than that under the noninformative prior. As far as the comparison of loss functions is concerned, ELF is better for the estimation of both of the model parameters. Figures 1–6 also confirm these findings.Table 1
BEs and PRs under SELF and noninformative prior usingλ=0.1,0.5 and γ=0.1,0.5.
nλ=0.1γ=0.1λ=0.1γ=0.5λ=0.5γ=0.1200.1572540.1855890.1372330.6506500.8667550.2539960.0103080.0094650.0013530.0169900.1477380.013761300.1314360.1579230.1122640.4767650.8494950.1845190.0044950.0049110.0007100.0156890.1422480.004290500.1038880.1319960.0924790.5149520.8040730.1582160.0012580.0024080.0002940.0100940.0846030.001585700.1158400.1106480.0950850.5097780.5947570.1143150.0012370.0014780.0002240.0070720.0362820.0012531000.0991650.1088670.0996490.5223620.4481780.0880120.0007000.0008940.0001280.0048450.0179500.000862Table 2
BEs and PRs under PLF and noninformative prior usingλ=0.1,0.5 and γ=0.1,0.5.
nλ=0.1γ=0.1λ=0.1γ=0.5λ=0.5γ=0.1200.1973660.1820090.1324710.7608751.2282400.2669860.1579230.0599020.0195440.0792080.3833880.079540300.1819230.1718540.1172230.6708270.9093630.1978810.0670850.0366320.0065470.1000080.1387130.030437500.1683800.1673980.1120320.5767440.7061920.1744740.0275640.0302680.0034480.0255140.1210100.026254700.1255180.1233530.1114160.4644530.6169730.1545740.0209340.0145980.0036780.0470470.0651710.0116821000.1171520.1181590.1074430.4797540.5131040.1189030.0116490.0145470.0014310.0103310.0622560.007771Table 3
BEs and PRs under QLF and noninformative prior usingλ=0.1,0.5 and γ=0.1,0.5.
nλ=0.1γ=0.1λ=0.1γ=0.5λ=0.5γ=0.1200.2656420.2335810.1543770.3444050.0513820.0193150.0686210.0586330.2282670.2159570.7844340.784260300.1727350.1933080.0601300.5750120.0921650.0337850.0617140.0402540.0995290.0589810.5228150.556817500.1265200.1644370.1176630.5231120.3831780.1346790.0233750.0350050.0395670.0551680.1219010.120160700.1047090.1085980.1087080.5197470.4303610.1179920.0187650.0174720.0294870.0326700.1036220.0940631000.1026160.1057730.1014360.5102750.4623150.1110160.0175470.0165160.0154260.0387280.0669650.092619Table 4
BEs and PRs under ELF and noninformative prior usingλ=0.1,0.5 and γ=0.1,0.5,0.1.
nλ=0.1γ=0.1λ=0.1γ=0.5λ=0.5γ=0.1200.1896980.2081990.1146470.4144120.6631130.0741510.1061720.2187580.0236350.0743390.3380680.415012300.1413980.1448900.1102470.5432230.6317510.1128220.0877380.1252400.0200130.0344070.0956080.135670500.1071900.1050970.1070170.5175960.5540930.1034590.0808970.0939470.0154190.0186770.0588400.080647700.0937870.0982410.0961260.4975390.5463760.0865340.0692830.0731070.0095440.0262460.0500550.0515661000.1043100.1002400.1023070.4150120.4916800.1089940.0286070.0414350.0073230.0409700.0467900.030830Table 5
BEs and PRs under SELF and informative prior usingλ=0.1,0.5 and γ=0.1,0.5.
nλ=0.1γ=0.1λ=0.1γ=0.5λ=0.5γ=0.1200.1697850.1762040.1210900.6126550.5673220.1520330.0083080.0087260.0010640.0303190.1243570.014980300.1490470.1342340.1091110.4499490.5339770.0791380.0040140.0028390.0005810.0140650.0758690.005891500.1141440.1170500.0942920.4691820.4867640.0911170.0015390.0018410.0002970.0087640.0351800.001895700.0886910.0953010.1284270.5165710.5075310.0923190.0009530.0009510.0002570.0070150.0314360.0013131000.1046420.1028930.0966130.4844670.5027930.5017570.0008420.0007760.0001410.0045950.0161380.001067Table 6
BEs and PRs under PLF and informative prior usingλ=0.1,0.5 and γ=0.1,0.5.
nλ=0.1γ=0.1λ=0.1γ=0.5λ=0.5γ=0.1200.2037550.2533040.1161200.6629230.9280860.1745030.0066860.0109150.0107230.0657670.2055450.036420300.1433970.1570530.0880550.5230880.7155460.1513500.0053760.0045450.0079100.0327850.1260930.035820500.1149740.1313420.0931880.5194360.6020930.1113990.0014590.0021370.0049490.0193710.0637680.015857700.0952530.0864590.1014970.5049660.4190850.0978500.0013630.0009280.0044720.0188760.0483920.0134541000.1046210.1036570.0985660.4983820.5641890.0994890.0007130.0008510.0023580.0182320.0328030.007902Table 7
BEs and PRs under QLF and informative prior usingλ=0.1,0.5 and γ=0.1,0.5.
nλ=0.1γ=0.1λ=0.1γ=0.5λ=0.5γ=0.1200.0319170.0211450.0699260.2500910.3176370.0503360.4383360.5151070.1560160.2995450.2320900.459284300.0344000.0225180.0725370.5400220.4110840.0664570.3090410.2915280.1253720.0584060.2219730.330387500.0707670.0662010.0891790.5232810.4311810.0723560.1624260.2140980.0269380.0372790.1072170.270354700.0775080.1559970.1094250.4950280.5589970.0738100.0789480.1664010.0260900.0188450.0951720.1134941000.1120700.0894520.0979470.4954520.5095060.1067960.0742270.1554390.0001450.0066040.0731670.079320Table 8
BEs and PRs under ELF and informative prior usingλ=0.1,0.5 and γ=0.1,0.5.
nλ=0.1γ=0.1λ=0.1γ=0.5λ=0.5γ=0.1200.1176250.1733240.1521850.5555330.0916340.0170410.0394660.0385570.0309470.0516040.2026050.305823300.1352090.1486230.0831860.5313430.3915560.0723220.0909610.1197850.0300840.0345990.1855380.210423500.0747730.0548190.1144840.4704470.4096480.0828870.0653210.1131630.0121130.0208010.0832860.071606700.1199680.1124880.0930790.4756030.5348280.1074320.0390460.0787660.0147880.0139700.0454440.0593651000.1014680.0982250.0948190.5119750.4428270.0960370.0020850.0577270.0167710.0103640.0286940.037519Table 9
BEs and PEs under ELF and informative priorλ=0.5,1 and γ=0.5,1,2.
nλ=0.5γ=0.5λ=1γ=1λ=1γ=2200.5979870.6085350.7262610.6825251.0765901.6337100.0409990.0586600.0430680.0362240.0230580.051033300.5727120.4280401.1451801.1286900.9470921.8160100.0315130.0550390.0168380.0189570.0189690.043565500.5549410.4401530.9124210.9161080.9654281.9340900.0217300.0385840.0131680.0132220.0102070.010319700.4646670.4633720.9291140.9310330.9745511.9574000.0118650.0173630.0107200.0133750.0072040.0114051000.5294650.5124660.9609240.9540531.0100802.0166300.0075490.0108750.0080350.0058090.0004100.005855Table 10
BEs and PRs under ELF and informative priorλ=1.5,2 and γ=1,2.
nλ=1.5γ=1λ=2γ=1λ=2γ=2201.3992800.7423991.6941101.3728801.4282601.7772900.0534970.0448370.0254750.0257660.0401750.030803301.4384401.0938502.2634200.7164141.7949702.1694200.0310820.0423970.0124490.0255640.0164070.016641501.4496300.9016761.9296501.0611301.8619601.9152400.0205120.0196250.0123370.0206470.0102850.014451701.4578600.9240602.0447700.9598951.9497201.9270300.0153600.0099560.0083840.0129760.0040610.0090091001.4789700.9622161.9875700.9871962.0261001.9415000.0044280.0079360.0050000.0093140.0029720.001045Table 11
BEs and PRs under ELF and informative priorλ=10,1 and γ=1,10.
nλ=10γ=1λ=1γ=10λ=10γ=10205.4226100.6676641.3913103.5119309.4072805.4741900.0288240.0548790.0281800.0275500.0735390.024276306.3304700.7777741.2256704.5549409.7136805.6612000.0199230.0329140.0200720.0215280.0396010.023192508.2929700.8379741.1608005.34376010.0662006.8149000.0059050.0301910.0098090.0143630.0232190.015703709.4437300.9152811.0848306.85540010.0321007.0771700.0051540.0052830.0049680.0121200.0131490.0132931009.7449900.9321571.0647406.8383109.9835107.9440300.0037410.0015810.0085910.0087310.0176310.012076Figure 1
Graph of BEs under different LFs using NIP forλ=0.1, using γ=0.1.Figure 2
Graph of PRs under different LFs using NIP forλ=0.1, using γ=0.1.Figure 3
Graph of BEs under different LFs using NIP forλ=0.1, using γ=0.5.Figure 4
Graph of PRs under different LFs using NIP forλ=0.1, using γ=0.5.Figure 5
Graph of BEs under different LFs using NIP forγ=0.1, using λ=0.5.Figure 6
Graph of PRs under different LFs using NIP forγ=0.1, using λ=0.5.


### 3.1. Real Data Analysis

In this subsection, the performance of the BIID has been compared with different classes of the lifetime distributions in modeling the real dataset. The real dataset contains lifetimes of 20 electronic components reported by Murthy et al. [25]. The values of the dataset are 0.03, 0.12, 0.22, 0.35, 0.73, 0.79, 1.25, 1.41, 1.52, 1.79, 1.80, 1.94, 2.38, 2.40, 2.87, 2.99, 3.14, 3.17, 4.72, and 5.09. We have named this data as dataset-1. The analysis using the real dataset has been presented in Tables 12–18. Another dataset containing failure times (in years) of electrical products has also been used for the analysis. These data contain the following observations: 0.0003, 0.0298, 0.1648, 0.3529, 0.4044, 0.5712, 0.5808, 0.7607, 0.8188, 1.1296, 1.2228, 1.2773, 1.9115, 2.2333, 2.3791, 3.0916, 3.4999, 3.7744, 7.4339, and 13.6866. These data have also been reported by Murthy et al. [25] and have been named as dataset-2. The results regarding analysis of dataset-2 have been placed in Tables 19–25.Table 12
Comparison of BIID with class of EGDs using real dataset-1.
ModelAICBICLLKS-statisticKSP valueBIID72.7874.77-34.390.090.98E exponential70.2272.21-33.110.150.65E Rayleigh67.6169.60-31.800.110.90E Weibull68.9571.94-31.470.110.92E gamma71.1074.09-32.550.140.75E log normal80.8983.87-37.440.250.12E Burr XII79.4182.39-36.700.220.20E chi-square70.3472.34-33.170.150.69E Frechet89.4392.42-41.710.230.17E Gompertz68.8671.85-31.430.110.92E log Frechet69.3172.30-31.650.090.98E Lomax73.0976.08-33.540.170.50E log-logistic69.3072.29-31.650.090.99AIC: Akaike information criteria; BIC: Bayesian information criteria; LL: log-likelihood; KS: Kolmogorov-Smirnov.Table 13
Comparison of BIID with class of BEGDs using real dataset-1.
ModelAICBICLLKS-statisticKSP valueBIID72.7874.77-34.390.090.98BE exponential74.5778.55-33.280.150.64BE Rayleigh72.4676.45-32.230.130.81BE Weibull72.9777.95-31.480.100.96BE gamma73.4778.45-31.730.120.86BE log normal75.0179.99-32.500.150.67BE Burr XII83.7788.75-36.880.210.25BE chi-square71.1575.13-31.570.100.95BE Frechet86.9091.87-38.450.230.19BE Gompertz73.1678.14-31.580.080.99BE log Frechet74.6979.67-32.340.090.98BE Lomax77.8182.79-33.900.160.61BE log-logistic73.0478.02-31.520.100.96Table 14
Comparison of BIID with class of WEGDs using real dataset-1.
ModelAICBICLLKS-statisticKSP valueBIID72.7874.77-34.390.090.98WE exponential73.8077.78-32.900.120.87WE Rayleigh77.4082.55-33.300.100.93WE Weibull74.4779.45-32.230.120.86WE gamma77.3682.34-33.680.110.92WE log normal74.2579.23-32.120.120.90WE Burr XII77.7082.68-33.850.140.75WE chi-square75.0879.06-33.540.130.87WE Frechet77.5382.51-33.760.110.93WE Gompertz76.2281.19-33.110.120.84WE log Frechet74.2579.23-32.120.110.92WE Lomax76.5581.53-33.270.110.92WE log-logistic76.9281.89-33.460.110.91Table 15
Comparison of BIID with class of EGGDs using real dataset-1.
ModelAICBICLLKS-statisticKSP valueBIID72.7874.77-34.390.090.98EG exponential72.2275.20-33.110.150.63EG Rayleigh69.5072.48-31.750.110.92EG Weibull70.9374.92-31.460.130.80EG gamma71.6775.65-31.830.120.85EG log normal74.3978.37-33.190.170.51EG BIID81.4185.39-36.700.230.19EG chi-square69.8372.82-31.910.100.96EG Frechet83.0587.03-37.520.250.13EG Gompertz70.8774.85-31.430.100.95EG log Frechet71.2775.25-31.630.110.915EG Burr XII72.7874.77-34.390.090.98EG exponential72.2275.20-33.110.150.63Table 16
Comparison of BIID with class of GGIDs using real dataset-1.
ModelAICBICLLKS-statisticKSP valueBIID72.7874.77-34.390.090.98GI exponential70.1472.13-33.070.150.67GI Rayleigh67.7669.75-31.880.110.93GI Weibull68.9571.94-31.470.100.94GI gamma69.8972.88-31.940.110.92GI log normal78.6381.61-36.310.240.16GI Burr XII78.9781.96-36.480.210.25GI chi-square70.3972.38-33.190.140.73GI Frechet87.0990.08-40.650.280.06GI Gompertz68.7971.78-31.390.980.01GI log Frechet69.3872.37-31.690.090.98GI Lomax75.3079.28-33.650.160.60GI log-logistic69.0771.77-31.390.090.97Table 17
Comparison of BIID with class of GGIIDs using real dataset-1.
ModelAICBICLLKS-statisticKSP valueBIID72.7874.77-34.390.090.98GII exponential70.1672.15-33.080.980.01GII Rayleigh70.2472.23-33.120.990.01GII Weibull71.4674.45-32.730.990.01GII gamma72.1375.11-33.060.990.01GII log normal78.4781.46-36.230.990.01GII Burr XII79.0081.99-36.500.210.26GII chi-square70.3972.38-33.190.140.72GII Frechet84.8987.87-39.440.990.01GII Gompertz68.8571.84-31.420.100.96GII log Frechet69.6672.65-31.830.980.01GII Lomax73.9676.94-33.980.980.01GII log-logistic72.7275.71-33.360.990.01Table 18
Comparison of BIID with class of LTDs using real dataset-1.
ModelAICBICLLKS-statisticKSP valueBIID72.7874.77-34.390.090.98LT Weibull69.5771.56-32.780.120.53LT exponential70.4172.40-33.200.170.28LT Rayleigh79.0381.03-37.510.190.16LT gamma70.1472.13-33.070.150.25LT inverse Gaussian77.5079.49-36.750.210.01LT Pareto72.9984.98-39.490.380.01LT Maxwell97.8199.80-36.900.250.01LT Levy87.0088.99-31.500.290.01LT Laplace76.0678.05-36.030.100.81LT inverse gamma92.4094.39-34.200.300.01LT gamble79.9781.96-37.980.150.19LT chi-square70.3972.38-33.190.140.40LT inverse chi-square82.6484.63-39.3220.290.01LT half normal67.8669.85-31.930.090.95LT inverse Gaussian88.4990.48-42.240.380.01Table 19
Comparison of BT-IID with class of EGDs using dataset-2.
Name of distributionDataset-2AICBICLLKS-statisticKSP valueBurr II-60.61-58.62-32.300.160.68E exponential-61.90-59.78-32.880.170.60E Rayleigh-45.46-43.47-24.730.340.01E Weibull-59.36-56.37-32.680.160.62E gamma-59.90-56.92-32.950.170.56E log normal-66.64-63.65-36.320.140.79E Burr XII-59.75-56.76-32.870.240.19E chi-square-60.93-40.94-35.460.150.01E Frechet-71.25-68.27-38.620.110.95E Gompertz-64.46-61.48-35.230.190.41E log Frechet-60.17-57.19-33.080.190.40E Lomax-64.72-61.73-35.360.140.77E log-logistic-70.60-67.61-38.300.110.96Table 20
Comparison of BT-IID with class of BEGDs using dataset-2.
Name of distributionDataset-2AICBICLLKS-statisticKSP valueBurr II-60.61-58.62-32.300.160.68BE exponential-66.06-62.07-37.030.100.98BE Rayleigh-59.60-55.61-33.800.150.69BE Weibull-61.37-56.39-35.680.140.78BE gamma-65.26-60.28-37.630.130.83BE log normal-67.14-62.16-38.570.120.93BE Burr XII-61.25-56.27-35.620.120.93BE chi-square-59.12-55.14-33.560.190.42BE Frechet-67.45-62.47-38.720.130.84BE Gompertz-63.16-58.18-36.580.120.91BE log Frechet-64.45-59.47-37.220.120.91BE Lomax-56.96-51.98-33.480.160.67BE log-logistic-67.33-62.35-38.660.120.93Table 21
Comparison of BT-IID with class of WEGDs using dataset-2.
Name of distributionDataset-2AICBICLLKS-statisticKSP valueBurr II-60.61-58.6232.300.160.68WE Weibull-64.41-59.4337.200.230.20WEB Rayleigh-66.20-62.1036.051.000.01WE Weibull-65.46-59.4938.730.150.72WE gamma-62.85-56.8737.420.210.33WE log normal-67.17-58.8835.290.120.66WE Burr XII-64.10-58.1238.050.150.74WE chi-square-56.24-51.2633.120.280.06WE Frechet-66.17-60.2039.080.110.93WE Gompertz-61.26-59.5538.480.130.87WE log Frechet-59.58-53.6035.790.220.24WE Lomax-65.26-59.2938.630.160.68WE log-logistic-64.73-58.7638.360.130.86Table 22
Comparison of BT-IID with class of EGGDs using dataset-2.
Name of distributionDataset-2AICBICLLKS-statisticKSP valueBurr II-60.61-58.62-32.300.160.68EG exponential-59.95-56.96-32.970.150.69EG Rayleigh-44.55-41.56-25.270.310.03EG Weibull-59.05-55.06-33.520.180.47EG gamma-58.17-54.18-33.080.170.57EG log normal-63.99-60.01-35.990.120.92EG Burr XII-60.28-56.29-34.140.170.56EG chi-square-61.82-58.83-33.910.170.58EG Frechet-69.72-65.74-38.860.130.85EG Gompertz-64.52-60.53-36.260.140.77EG log Frechet-62.28-58.30-35.140.160.66EG Lomax-63.92-59.94-35.960.140.81EG log-logistic-68.15-64.16-38.070.120.89Table 23
Comparison of BT-IID with class of GGIDs using dataset-2.
Name of distributionDataset-2AICBICLLKS-statisticKSP valueBurr II-60.61-58.62-32.300.160.68GI exponential-55.78-53.79-29.890.220.26GI Rayleigh-44.59-42.60-24.290.340.01GI Weibull-53.14-50.15-29.570.250.15GI gamma-56.81-52.82-32.400.190.44GI log normal-62.54-59.56-34.270.150.71GI Burr XII-53.31-50.32-29.650.280.08GI chi-square-55.63-53.64-34.810.480.01GI Frechet-71.25-68.27-38.620.110.94GI Gompertz-51.37-48.38-28.680.260.11GI log Frechet-50.73-47.74-28.360.260.11GI Lomax-62.76-59.77-34.380.140.81GI log-logistic-69.85-66.86-37.920.100.97Table 24
Comparison of BT-IID with class of GGIDs using dataset-2.
Name of distributionDataset-2AICBICLLKS-statisticKSP valueBurr II-60.61-58.62-32.300.160.68GII exponential-42.61-40.62-23.300.970.01GII Rayleigh-47.58-45.59-25.790.990.12GII Weibull-54.91-51.92-30.450.990.01GII gamma-53.27-50.28-29.630.250.13GII log normal-59.33-56.35-32.660.990.01GII Burr XII-52.30-49.32-29.150.240.17GII chi-square-52.95-50.96-28.470.980.01GII Frechet-71.70-68.71-38.850.990.01GII Gompertz-49.99-47.00-27.990.280.07GII log Frechet-54.20-51.21-30.100.250.15GII Lomax-39.52-36.54-22.760.960.01GII log-logistic-70.20-67.21-38.100.990.01Table 25
Comparison of BT-IID with class of LTDs using dataset-2.
Name of distributionDataset-2AICBICLLKS-statisticKSP valueBurr II-60.61-58.6232.300.160.68LT Weibull-48.84-46.8526.420.110.62LT exponential-40.29-38.3022.140.120.61LT Rayleigh-46.55-44.5625.270.300.58LT gamma-55.78-53.7929.890.130.54LT inverse Gaussian-63.12-61.1333.560.120.54LT Pareto-75.12-73.1239.560.140.56LT Maxwell42.5340.5423.260.150.66LT Levy-89.23-91.2242.610.150.59LT Laplace-57.15-55.1530.570.110.61LT inverse gamma-67.96-65.9735.980.100.60LT gamble-18.11-16.1211.050.100.64LT chi-square-4.46-2.474.230.140.62LT inverse chi-square-66.49-64.5035.240.130.52LT half normal-42.98-40.9823.490.110.55LT inverse Gaussian-62.15-60.1533.070.100.61From Tables12–18, it can be assessed that performance of BIID is
(i)
Comparable with the class of exponentiated distributions(ii)
Comparable with the class of beta extended distributions(iii)
Slightly better than the Weibull extended generalized family of distributions(iv)
Comparable with the exponentiated generalized G distributions(v)
Comparable with the gamma G I distributions(vi)
Comparable with the gamma G II distributions(vii)
Comparable with the conventional distributionsThe comparison of BIID with class of lifetime distribution has been made using three criteria, i.e., AIC, BIC, and KS tests. Whenever the BIID was better in modeling the respective real dataset using all three criteria, the BIID was declared better to the corresponding class of life models. On the other hand, whenever the BIID was better to some but not all the modes in the respective class, the BIID distribution was considered comparable to the respective class of lifetime models. Keeping in view that the different classes of distribution contain higher number of parameters (from two to six), the BIID can provide lots of ease in modeling lifetimes regarding the real data containing lifetimes of 20 electronic components. Similar trends can be seen from the analysis of the dataset-2, reported in Tables19–25.


## 3.1. Real Data Analysis

In this subsection, the performance of the BIID has been compared with different classes of the lifetime distributions in modeling the real dataset. The real dataset contains lifetimes of 20 electronic components reported by Murthy et al. [25]. The values of the dataset are 0.03, 0.12, 0.22, 0.35, 0.73, 0.79, 1.25, 1.41, 1.52, 1.79, 1.80, 1.94, 2.38, 2.40, 2.87, 2.99, 3.14, 3.17, 4.72, and 5.09. We have named this data as dataset-1. The analysis using the real dataset has been presented in Tables 12–18. Another dataset containing failure times (in years) of electrical products has also been used for the analysis. These data contain the following observations: 0.0003, 0.0298, 0.1648, 0.3529, 0.4044, 0.5712, 0.5808, 0.7607, 0.8188, 1.1296, 1.2228, 1.2773, 1.9115, 2.2333, 2.3791, 3.0916, 3.4999, 3.7744, 7.4339, and 13.6866. These data have also been reported by Murthy et al. [25] and have been named as dataset-2. The results regarding analysis of dataset-2 have been placed in Tables 19–25.Table 12
Comparison of BIID with class of EGDs using real dataset-1.
ModelAICBICLLKS-statisticKSP valueBIID72.7874.77-34.390.090.98E exponential70.2272.21-33.110.150.65E Rayleigh67.6169.60-31.800.110.90E Weibull68.9571.94-31.470.110.92E gamma71.1074.09-32.550.140.75E log normal80.8983.87-37.440.250.12E Burr XII79.4182.39-36.700.220.20E chi-square70.3472.34-33.170.150.69E Frechet89.4392.42-41.710.230.17E Gompertz68.8671.85-31.430.110.92E log Frechet69.3172.30-31.650.090.98E Lomax73.0976.08-33.540.170.50E log-logistic69.3072.29-31.650.090.99AIC: Akaike information criteria; BIC: Bayesian information criteria; LL: log-likelihood; KS: Kolmogorov-Smirnov.Table 13
Comparison of BIID with class of BEGDs using real dataset-1.
ModelAICBICLLKS-statisticKSP valueBIID72.7874.77-34.390.090.98BE exponential74.5778.55-33.280.150.64BE Rayleigh72.4676.45-32.230.130.81BE Weibull72.9777.95-31.480.100.96BE gamma73.4778.45-31.730.120.86BE log normal75.0179.99-32.500.150.67BE Burr XII83.7788.75-36.880.210.25BE chi-square71.1575.13-31.570.100.95BE Frechet86.9091.87-38.450.230.19BE Gompertz73.1678.14-31.580.080.99BE log Frechet74.6979.67-32.340.090.98BE Lomax77.8182.79-33.900.160.61BE log-logistic73.0478.02-31.520.100.96Table 14
Comparison of BIID with class of WEGDs using real dataset-1.
ModelAICBICLLKS-statisticKSP valueBIID72.7874.77-34.390.090.98WE exponential73.8077.78-32.900.120.87WE Rayleigh77.4082.55-33.300.100.93WE Weibull74.4779.45-32.230.120.86WE gamma77.3682.34-33.680.110.92WE log normal74.2579.23-32.120.120.90WE Burr XII77.7082.68-33.850.140.75WE chi-square75.0879.06-33.540.130.87WE Frechet77.5382.51-33.760.110.93WE Gompertz76.2281.19-33.110.120.84WE log Frechet74.2579.23-32.120.110.92WE Lomax76.5581.53-33.270.110.92WE log-logistic76.9281.89-33.460.110.91Table 15
Comparison of BIID with class of EGGDs using real dataset-1.
ModelAICBICLLKS-statisticKSP valueBIID72.7874.77-34.390.090.98EG exponential72.2275.20-33.110.150.63EG Rayleigh69.5072.48-31.750.110.92EG Weibull70.9374.92-31.460.130.80EG gamma71.6775.65-31.830.120.85EG log normal74.3978.37-33.190.170.51EG BIID81.4185.39-36.700.230.19EG chi-square69.8372.82-31.910.100.96EG Frechet83.0587.03-37.520.250.13EG Gompertz70.8774.85-31.430.100.95EG log Frechet71.2775.25-31.630.110.915EG Burr XII72.7874.77-34.390.090.98EG exponential72.2275.20-33.110.150.63Table 16
Comparison of BIID with class of GGIDs using real dataset-1.
ModelAICBICLLKS-statisticKSP valueBIID72.7874.77-34.390.090.98GI exponential70.1472.13-33.070.150.67GI Rayleigh67.7669.75-31.880.110.93GI Weibull68.9571.94-31.470.100.94GI gamma69.8972.88-31.940.110.92GI log normal78.6381.61-36.310.240.16GI Burr XII78.9781.96-36.480.210.25GI chi-square70.3972.38-33.190.140.73GI Frechet87.0990.08-40.650.280.06GI Gompertz68.7971.78-31.390.980.01GI log Frechet69.3872.37-31.690.090.98GI Lomax75.3079.28-33.650.160.60GI log-logistic69.0771.77-31.390.090.97Table 17
Comparison of BIID with class of GGIIDs using real dataset-1.
ModelAICBICLLKS-statisticKSP valueBIID72.7874.77-34.390.090.98GII exponential70.1672.15-33.080.980.01GII Rayleigh70.2472.23-33.120.990.01GII Weibull71.4674.45-32.730.990.01GII gamma72.1375.11-33.060.990.01GII log normal78.4781.46-36.230.990.01GII Burr XII79.0081.99-36.500.210.26GII chi-square70.3972.38-33.190.140.72GII Frechet84.8987.87-39.440.990.01GII Gompertz68.8571.84-31.420.100.96GII log Frechet69.6672.65-31.830.980.01GII Lomax73.9676.94-33.980.980.01GII log-logistic72.7275.71-33.360.990.01Table 18
Comparison of BIID with class of LTDs using real dataset-1.
ModelAICBICLLKS-statisticKSP valueBIID72.7874.77-34.390.090.98LT Weibull69.5771.56-32.780.120.53LT exponential70.4172.40-33.200.170.28LT Rayleigh79.0381.03-37.510.190.16LT gamma70.1472.13-33.070.150.25LT inverse Gaussian77.5079.49-36.750.210.01LT Pareto72.9984.98-39.490.380.01LT Maxwell97.8199.80-36.900.250.01LT Levy87.0088.99-31.500.290.01LT Laplace76.0678.05-36.030.100.81LT inverse gamma92.4094.39-34.200.300.01LT gamble79.9781.96-37.980.150.19LT chi-square70.3972.38-33.190.140.40LT inverse chi-square82.6484.63-39.3220.290.01LT half normal67.8669.85-31.930.090.95LT inverse Gaussian88.4990.48-42.240.380.01Table 19
Comparison of BT-IID with class of EGDs using dataset-2.
Name of distributionDataset-2AICBICLLKS-statisticKSP valueBurr II-60.61-58.62-32.300.160.68E exponential-61.90-59.78-32.880.170.60E Rayleigh-45.46-43.47-24.730.340.01E Weibull-59.36-56.37-32.680.160.62E gamma-59.90-56.92-32.950.170.56E log normal-66.64-63.65-36.320.140.79E Burr XII-59.75-56.76-32.870.240.19E chi-square-60.93-40.94-35.460.150.01E Frechet-71.25-68.27-38.620.110.95E Gompertz-64.46-61.48-35.230.190.41E log Frechet-60.17-57.19-33.080.190.40E Lomax-64.72-61.73-35.360.140.77E log-logistic-70.60-67.61-38.300.110.96Table 20
Comparison of BT-IID with class of BEGDs using dataset-2.
Name of distributionDataset-2AICBICLLKS-statisticKSP valueBurr II-60.61-58.62-32.300.160.68BE exponential-66.06-62.07-37.030.100.98BE Rayleigh-59.60-55.61-33.800.150.69BE Weibull-61.37-56.39-35.680.140.78BE gamma-65.26-60.28-37.630.130.83BE log normal-67.14-62.16-38.570.120.93BE Burr XII-61.25-56.27-35.620.120.93BE chi-square-59.12-55.14-33.560.190.42BE Frechet-67.45-62.47-38.720.130.84BE Gompertz-63.16-58.18-36.580.120.91BE log Frechet-64.45-59.47-37.220.120.91BE Lomax-56.96-51.98-33.480.160.67BE log-logistic-67.33-62.35-38.660.120.93Table 21
Comparison of BT-IID with class of WEGDs using dataset-2.
Name of distributionDataset-2AICBICLLKS-statisticKSP valueBurr II-60.61-58.6232.300.160.68WE Weibull-64.41-59.4337.200.230.20WEB Rayleigh-66.20-62.1036.051.000.01WE Weibull-65.46-59.4938.730.150.72WE gamma-62.85-56.8737.420.210.33WE log normal-67.17-58.8835.290.120.66WE Burr XII-64.10-58.1238.050.150.74WE chi-square-56.24-51.2633.120.280.06WE Frechet-66.17-60.2039.080.110.93WE Gompertz-61.26-59.5538.480.130.87WE log Frechet-59.58-53.6035.790.220.24WE Lomax-65.26-59.2938.630.160.68WE log-logistic-64.73-58.7638.360.130.86Table 22
Comparison of BT-IID with class of EGGDs using dataset-2.
Name of distributionDataset-2AICBICLLKS-statisticKSP valueBurr II-60.61-58.62-32.300.160.68EG exponential-59.95-56.96-32.970.150.69EG Rayleigh-44.55-41.56-25.270.310.03EG Weibull-59.05-55.06-33.520.180.47EG gamma-58.17-54.18-33.080.170.57EG log normal-63.99-60.01-35.990.120.92EG Burr XII-60.28-56.29-34.140.170.56EG chi-square-61.82-58.83-33.910.170.58EG Frechet-69.72-65.74-38.860.130.85EG Gompertz-64.52-60.53-36.260.140.77EG log Frechet-62.28-58.30-35.140.160.66EG Lomax-63.92-59.94-35.960.140.81EG log-logistic-68.15-64.16-38.070.120.89Table 23
Comparison of BT-IID with class of GGIDs using dataset-2.
Name of distributionDataset-2AICBICLLKS-statisticKSP valueBurr II-60.61-58.62-32.300.160.68GI exponential-55.78-53.79-29.890.220.26GI Rayleigh-44.59-42.60-24.290.340.01GI Weibull-53.14-50.15-29.570.250.15GI gamma-56.81-52.82-32.400.190.44GI log normal-62.54-59.56-34.270.150.71GI Burr XII-53.31-50.32-29.650.280.08GI chi-square-55.63-53.64-34.810.480.01GI Frechet-71.25-68.27-38.620.110.94GI Gompertz-51.37-48.38-28.680.260.11GI log Frechet-50.73-47.74-28.360.260.11GI Lomax-62.76-59.77-34.380.140.81GI log-logistic-69.85-66.86-37.920.100.97Table 24
Comparison of BT-IID with class of GGIDs using dataset-2.
Name of distributionDataset-2AICBICLLKS-statisticKSP valueBurr II-60.61-58.62-32.300.160.68GII exponential-42.61-40.62-23.300.970.01GII Rayleigh-47.58-45.59-25.790.990.12GII Weibull-54.91-51.92-30.450.990.01GII gamma-53.27-50.28-29.630.250.13GII log normal-59.33-56.35-32.660.990.01GII Burr XII-52.30-49.32-29.150.240.17GII chi-square-52.95-50.96-28.470.980.01GII Frechet-71.70-68.71-38.850.990.01GII Gompertz-49.99-47.00-27.990.280.07GII log Frechet-54.20-51.21-30.100.250.15GII Lomax-39.52-36.54-22.760.960.01GII log-logistic-70.20-67.21-38.100.990.01Table 25
Comparison of BT-IID with class of LTDs using dataset-2.
Name of distributionDataset-2AICBICLLKS-statisticKSP valueBurr II-60.61-58.6232.300.160.68LT Weibull-48.84-46.8526.420.110.62LT exponential-40.29-38.3022.140.120.61LT Rayleigh-46.55-44.5625.270.300.58LT gamma-55.78-53.7929.890.130.54LT inverse Gaussian-63.12-61.1333.560.120.54LT Pareto-75.12-73.1239.560.140.56LT Maxwell42.5340.5423.260.150.66LT Levy-89.23-91.2242.610.150.59LT Laplace-57.15-55.1530.570.110.61LT inverse gamma-67.96-65.9735.980.100.60LT gamble-18.11-16.1211.050.100.64LT chi-square-4.46-2.474.230.140.62LT inverse chi-square-66.49-64.5035.240.130.52LT half normal-42.98-40.9823.490.110.55LT inverse Gaussian-62.15-60.1533.070.100.61From Tables12–18, it can be assessed that performance of BIID is
(i)
Comparable with the class of exponentiated distributions(ii)
Comparable with the class of beta extended distributions(iii)
Slightly better than the Weibull extended generalized family of distributions(iv)
Comparable with the exponentiated generalized G distributions(v)
Comparable with the gamma G I distributions(vi)
Comparable with the gamma G II distributions(vii)
Comparable with the conventional distributionsThe comparison of BIID with class of lifetime distribution has been made using three criteria, i.e., AIC, BIC, and KS tests. Whenever the BIID was better in modeling the respective real dataset using all three criteria, the BIID was declared better to the corresponding class of life models. On the other hand, whenever the BIID was better to some but not all the modes in the respective class, the BIID distribution was considered comparable to the respective class of lifetime models. Keeping in view that the different classes of distribution contain higher number of parameters (from two to six), the BIID can provide lots of ease in modeling lifetimes regarding the real data containing lifetimes of 20 electronic components. Similar trends can be seen from the analysis of the dataset-2, reported in Tables19–25.


## 4. Conclusion

Unlike other distributions from the family of Burr distributions, the lack of literature regarding the BIID motivated us to conduct an in-depth study regarding this distribution. We have proposed the Bayesian analysis for the parameters of the BIID assuming different priors (informative and noninformative) and loss functions (SELF, PLF, QLF, and ELF) to estimate the parameters of the proposed distribution. As the Bayes estimates for the said parameters cannot be obtained in the closed form, the numerical integration was used for the computations. The performance of the proposed estimators has been discussed in terms of associated posterior risks. The applications of the proposed model have been explored using real data. Finally, the performance of the distribution, in modeling real data, has been compared with different classes of conventional and modified distributions. These classes include LTD, BEGD, WEGD, ED, EKGD, GGID, GGIID, and EGD having ninety-nine (two to six parametric) distributions. The following distributions along with their modifications were used for comparison: Weibull, exponential, and Rayleigh, gamma, log normal, Pareto, Maxwell, Levy, Laplace, inverse gamma, Gompertz, chi-square, inverse chi-square, half normal, and log-logistic distribution. The comparison has been carried out using a real dataset. The AIC, BIC, and KS tests were used as goodness-of-fit criteria, for comparison.The highlights of the contribution have been presented in the following.(i)
It was encouraging to observe that the estimated values of the model parameters converged to the true figures, especially in the large samples. Likewise, the magnitudes of the posterior tend to become smaller in the larger samples. Therefore, the Bayes estimates from the BIID were consistent in nature under all priors and for all loss functions(ii)
The results under noninformative and informative priors were quite comparable, with slight advantages in case of informative priors. So, the Bayes estimators from the proposed distribution were insensitive with respect to change in prior parameters(iii)
The larger values for the parameterλ provided the improved estimation for parameter γ. On the other hand, smaller values of the parameter γ provided more efficient estimation for the parameter λ(iv)
The Bayesian estimates under ELF were better than their counterparts under all priors, all true parametric values, and for both parameters. Hence, the employment of ELF provided substantial gain in efficiencies(v)
The applications of the BIID have been demonstrated using ten real datasets. The modeling ability of this model has been compared with ninety-nine life distributions containing two to six parameters. From the said comparison, it can be assessed that the results under the BIID were almost as efficient as under the competing models having up to six parameters. Therefore, the BIID has been explored as an appealing alternative to the competing distributions with significant reduction in estimation complexitiesThe study can be extended for analyzing censored data while comparing BIID with other life distributions. The applicability of this distribution can also be explored in modeling the censored heterogeneous datasets.



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*Source: 6347192-2022-01-12.xml*