Journal of Probability and Statistics
Volume 2018 (2018), Article ID 3152807, 12 pages
https://doi.org/10.1155/2018/3152807

Research Article

The Half-Logistic Lomax Distribution for Lifetime Modeling

Masood Anwar and Jawaria Zahoor

Department of Mathematics, COMSATS Institute of Information Technology, Park Road, Chak Shahzad, Islamabad, Pakistan

Correspondence should be addressed to Masood Anwar; masoodanwar@comsats.edu.pk

Received 7 August 2017; Revised 30 November 2017; Accepted 14 December 2017; Published 1 February 2018

Academic Editor: Chin-Shang Li

Copyright © 2018 Masood Anwar and Jawaria Zahoor. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

We introduce a new two-parameter lifetime distribution called the half-logistic Lomax (HLL) distribution. The proposed distribution is obtained by compounding half-logistic and Lomax distributions. We derive some mathematical properties of the proposed distribution such as the survival and hazard rate function, quantile function, mode, median, moments and moment generating functions, mean deviations from mean and median, mean residual life function, order statistics, and entropies. The estimation of parameters is performed by maximum likelihood and the formulas for the elements of the Fisher information matrix are provided. A simulation study is run to assess the performance of maximum-likelihood estimators (MLEs). The flexibility and potentiality of the proposed model are illustrated by means of real and simulated data sets.

1. Introduction

The commonly used lifetime distributions (exponential, gamma, Weibull, Lomax, lognormal, etc.) have a limited range of behavior and do not provide adequate fit to complex data sets in different sciences. Generalizations of these distributions offer more flexibility and provide reasonable parametric fits to complex data sets. Motivated by the various applications of Lomax and half-logistic distributions in areas of income and wealth inequality, firm size, size of cities, queuing problems, actuarial science, medical and biological sciences, and engineering, we propose a two-parameter continuous lifetime distribution by compounding the half-logistic and the Lomax distribution called half-logistic Lomax (HLL) distribution.

The Lomax [1] (or Pareto Type-II) distribution was introduced to model business failure data. For more detail about the Lomax distribution, we refer the readers to Rady et al. [2], Tahir et al. [3], and the references therein. In literature, there are several generalizations of the Lomax distribution. Abdul-Moniem [4] developed the exponentiated Lomax distribution, and Al-Awadhi and Ghitany [5] introduced the discrete Poisson–Lomax distribution by using the Lomax distribution as a mixing distribution for the Poisson parameter. Asgharzadeh et al. [6] proposed the Pareto Poisson–Lindely distribution, and Cordeiro et al. [7] investigated the gamma-Lomax distribution and studied its properties. Ghitany et al. [8] and Gupta et al. [9] considered the Marshal–Olkin approach and extended the Lomax distribution, and Lemonte and Cordeiro [10] proposed and studied the McDonald-Lomax, the beta Lomax, and the Kumaraswamy Lomax distributions. Other models constitute flexible family of distributions in terms of the variates of shapes and hazard functions; see, for example, Al-Zahrani and Sagor [11], El-Bassiouny et al. [12], Rady et al. [2], Kilany [13], and Tahir et al. [3]. These generalizations of the Lomax distribution are considered to be useful life distribution models.

The cumulative distribution function (cdf) of Lomax distribution is given by

where is a shape parameter and is a scale parameter. The probability density function (pdf) corresponding to (1) is

Cordeiro et al. [14] define the cdf of the new type I half-logistic-G (TIHL-) family of distributions by

where is the baseline cdf depending on a parameter vector and an additional shape parameter . As a special case, if , then the TIHL- is the half-logistic- (HL-) distribution with cdf

The corresponding pdf to (4) is given by

This paper aims to provide a new lifetime model with a minimum number of parameters by compounding the half-logistic and the Lomax distribution called half-logistic Lomax (HLL) distribution. The proposed distribution is heavy-tailed and has a decreasing or upside-down bathtub (or unimodal) shaped hazard rates depending on its parameters. Upside-down bathtub shaped hazard rates are common in reliability, engineering, and survival analysis. The HLL distribution can also be applied in engineering as the Lomax [1] distribution and can be a useful alternative to other well-known densities in lifetime applications. It is interesting to note that the HLL distribution is a special case of Marshall-Olkin–Lomax distribution introduced by Ghitany et al. [8]. We obtain some mathematical properties of the proposed distribution and parameters of the model are estimated by the maximum-likelihood estimation method.

The rest of this paper is organized as follows. In Section 2, we introduce the half-logistic Lomax distribution and provide plots of its density function. In Section 3, we investigate various mathematical properties of the HLL distribution including survival and hazard rate function, quantile function, moments, mean residual life function, mean deviation from the mean and the mean deviation from the median, entropies, and order statistics. In Section 4, estimation of parameters is given by MLE method and the asymptotic distribution of the estimators is studied via Fisher’s information matrix. Simulation results on the behavior of the MLEs are presented in Section 5. A real data application is conducted in Section 6. Finally, in Section 7, we conclude that the HLL distribution is the best model as compared to other competing models.

2. The HLL Distribution

We define the half-logistic-Lomax (HLL) density function by inserting (1) and (2) into (5). So, we obtain

The corresponding cumulative density function (cdf) follows from (1) and (4) and is given by

Hereafter, we will denote a random variable having pdf (6) by . The limit of the HLL density (6) as is 0 and the limit as is . Figure 1 depicts some of the possible shapes of density (6) for selected parameter values. The mode of density (6) is obtained from solving , which is given by

Figure 1: Plots of the HLL pdf for some parameter values.

3. Mathematical Properties

This section describes mathematical properties of the HLL distribution.

3.1. The Survivor and Hazard Rate Functions

Using (6) and (7), the survival function (sf) and hazard rate function (hrf) of are, respectively, given by

The limit of as is ; that is, is bounded from below and continuous in its parameters. The limit of as is 0. According to Glaser [15], we determine the parameter intervals for which the hazard rate function of the HLL distribution is decreasing or upside-down bathtub. Let

Then its first derivative is given by

If , then for all . Then the hazard rate function is decreasing. By Glaser’s theorem [15], it is sufficient to show that there exists such that for all . implies that and for all , which implies unimodal shape of the hazard rate function. For , for all and for all . Thus, the hazard rate function is upside-down bathtub-shaped for . Figure 2 illustrates some of the possible shapes of hazard rate function for selected values of the parameters.

(a)

(b)

Figure 2: Plots of the HLL hrf for some parameter values.

3.2. Quantile and Random Number Generation

The cumulative distribution function is given by (7). Inverting , we obtain

Equation (12) can be used to simulate the HLL variable.

3.3. Moments

The following theorem gives moments of the HLL distribution and its mean moments and cumulants.

Theorem 1. The th moment about the origin of is given by

Proof. We have

Setting , yields

Corollary 2. The first four moments of are given, respectively, by

Proof. Putting in (13) yields the desired result.
The mean moments and cumulants of can be obtained by the following relation;

Here . We have , , , and so on.
The skewness and kurtosis follow from the second, third, and fourth cumulants.
The variance of , denoted by , is given by

The th descending factorial moment of is

where

is the Sterling number of the first kind which counts the number of ways to permute a list of items into cycles.

Theorem 3. The moment generating function of is given by

Proof. We have

Setting , yields

After simplification, we get

3.4. Mean Residual Life (MRL) Function

The MRL function is important in reliability, survival analysis, actuarial sciences, economics, and social sciences for characterizing lifetime distributions. It also plays an important role in repair and replacement strategies and summarizes the entire residual life function.

Theorem 4. The MRL function of is given by

Proof. The MRL function is defined as

Therefore, we have

Setting , yields

After simplification, we get (25).

3.5. Mean Deviations

The mean deviations about mean and median are, respectively, defined by

Theorem 5. The mean deviations about mean and mean deviation about median of are given by

Proof. Let .
By inserting pdf of the HLL into (29) and setting , , we get

Using the series representations , where is positive integer, we get

Setting into (29) and after some manipulations we get the desired result.
Similarly, the measure can be obtained.

3.6. Entropies

The entropy of a random variable is a measure of variation of uncertainty. Two popular entropies are the Rényi and Shannon entropies.

The Rényi entropy is defined as

The Rényi entropy for the HLL distribution is given by

Setting , yields

Thus, we have

Another entropy measure defined by is known as the Shannon entropy and plays a similar role as the kurtosis measure in comparing the shapes of densities and measuring tail heaviness. We have

By using the binomial series expansion and   (2.727) of Gradshteyn and Ryzhik [16], it follows that

Hence, the Shannon entropy can be expressed in the form

3.7. Order Statistics

Let be a random sample of size from a distribution with pdf and cdf . Let denote the corresponding order statistics. Then the pdf and cdf of are

where is the complete beta function.

Theorem 6. Let and be the pdf and cdf of . Then the pdf and cdf of are

Proof. Inserting (6) and (7) into (44), we get the result.

4. Estimation and Asymptotic Distribution

We consider the maximum-likelihood estimation of the parameters , . The distributional properties of , are obtained using Fisher information matrix.

4.1. Maximum-Likelihood Estimation

Let be a random sample of size from distribution. Then the corresponding likelihood function is

The log-likelihood function is given by

Calculating the first-order partial derivatives of (46) with respect to , and equating them to zero, we get the following nonlinear equations:

To find out the maximum-likelihood estimates (MLEs) of , , we have to solve the above nonlinear equations. Apparently, there is no closed form solution in , . We have to use a numerical technique method, such as Newton-Raphson method, to obtain the solution.

4.2. Asymptotic Distribution

The normal approximation for the large-sample for the MLE can be used to compute confidence intervals (CIs). For finding the observed information matrix of , , we compute the second-order partial derivatives of (46) in the appendix.

The variance-covariance matrix may be approximated by , where is the observed information matrix. Since involves the parameters , , we replace the parameters by the corresponding MLEs in order to obtain an estimate of , which is denoted by , where , when , is replaced by , . Using these results, an approximate CI for , , respectively, is given by

where is the upper percentile of the standard normal distribution.

5. Simulation Study

The assessment of the maximum-likelihood estimates is based on a simulation study. The following steps were followed:

(1)Generate 5,000 samples of size from (6). The HLL variables are generated using given in (12), where .(2)Compute the MLEs for the 5,000 samples, say for (3)Compute the mean square errors (MSE) for each parameter.

We repeat these steps for with , . The comparison is based on MSEs. Figure 3 plots the MSEs of the MLEs of two parameters. The assessment based on this simulation study is that the MSEs for each parameter decrease to zero with increasing sample size.

Figure 3: Plots of the and for , based on 10,000 replications.

6. Application

6.1. Cancer Data

We have considered a real data set, which records the remission times (in months) of a random sample of 128 bladder cancer patients reported in [17]. We have fitted the HLL distribution to the data set using MLE and compared the proposed distribution with the following distributions:

(i)The Lomax distribution, introduced by Lomax [1], with pdf is

(ii)The McLomax distribution, introduced by Lemonte and Cordeiro [10], with pdf is

(iii)The beta Lomax (BLomax) distribution is a submodel of McLomax for with pdf

(iv)The Kumaraswamy Lomax (KwLomax) distribution is a submodel of McLomax for , with pdf

(v)The Exponentiated Standard Lomax (ESLomax) distribution, considered by Gupta et al. [18], with pdf is

(vi)The Lomax-Logarithmic (LL) distribution, introduced by Al-Zahrani and Sagor [11], with pdf is

For identification of the shape of the hazard function of the cancer data set, we use a graphical method based on the Total Time on Test (TTT) plot [19]. Let be a random variable with nonnegative values, which represents the survival time. The empirical TTT curve is constructed by plotting , against , where and are the order statistics of the sample. If the empirical TTT transform is straight diagonal, hrf is constant; if it is convex, hrf is decreasing; if it is concave, hrf is increasing; if it is convex and then concave, hrf is bathtub; and if it is concave and then convex, hrf is upside-down bathtub [19]. Figure 4 shows the empirical TTT plot for the data set. The shape of the data set is unimodal, as its TTT plot is concave and then convex. The model selection is carried out using Akaike information criterion (AIC), the Bayesian information criterion (BIC), the consistent Akaike information criterion (CAIC), and the Hannan-Quinn information criterion (HQIC).

where denotes the log-likelihood function evaluated at the MLEs, is the number of model parameters, and is the sample size. The model with the lowest values for these statistics could be chosen as the best model to fit the data. First, we describe the data set in Table 1. Then, we report the maximum-likelihood estimators (and the corresponding standard errors in parentheses) of the parameters and values of AIC, BIC, HQIC, and CAIC in Table 2. In Table 2, we provide some results reported in [10, 11]. We conclude that our new model HLL has the smallest AIC, BIC, HQIC, and CAIC values among all fitted models, and so it could be chosen as the best model. These results are obtained using the AdequacyModel package version 1.0.8 available for the programming language R [20].

Table 1: Descriptive statistics.

Mean
Median
Mode
SD
Variance
Skewness
Kurtosis
Min.
Max.

9.37
6.40
5.0
10.51
110.43
3.287
15.48
0.08
79.05

Table 2: MLEs (standard errors in parentheses) and the measures AIC, BIC, HQIC, and CAIC for cancer data.

Model
Estimates

Measures

AIC
BIC
HQIC
CAIC

HLL
5.2814
0.0367

827.69
833.40
830.01
827.79

(2.0847)
(0.0177)

Lomax
13.9384
121.0225

831.67
837.37
833.98
831.76

(15.368)
(142.714)

McLomax
0.8085
11.2929
1.5060
4.1886
2.1046
829.82
844.09
835.62
830.31

(3.364)
(15.818)
(0.234)
(25.029)
(3.079)

BLomax
3.9191
23.9281
1.5853
0.1572

828.14
839.55
832.78
828.46

(18.192)
(27.338)
(0.280)
(5.024)

KwLomax
0.3911
12.2973
1.5162
11.0323

827.88
839.29
832.52
828.21

(2.386)
(17.316)
(0.228)
(87.144)

ESLomax
1.0877

4.6575

856.61
862.31
833.98
856.71

(0.086)

(0.686)

LL
4.8696
20.1652

7.6938

830.88
839.44
834.36
831.07

(2.1317)
(18.3747)

(11.8362)

Figure 4: Empirical TTT plot for the cancer data.

6.2. Simulated Data

Here, we used a simulation study to check the flexibility of the proposed distribution. We have generated a sample of size from the six distributions and compared the fit of the HLL distribution with competing models.

The details of generated samples with the specified values of the parameters from the six models are as follows:

(1)Lomax distribution with and (see Table 3)(2)McLomax distribution with parameters , , , , and (see Table 4)(3)BLomax distribution with parameters , , , and (see Table 5)(4)KwLomax distribution with parameters , , , and (see Table 6)(5)ESLomax distribution with parameters and (see Table 7)(6)LL distribution with parameters , , and (see Table 8)

Table 3: MLEs (standard errors in parentheses) and the measures AIC, BIC, HQIC, and CAIC for simulated data from Lomax distribution with and .

Model
Estimates

Measures

AIC
BIC
HQIC
CAIC

HLL
1.8032
1.2386

257.03
262.24
259.14
257.16

(0.4230)
(0.4764)

Lomax
2.2441
0.5337

257.39
262.60
259.50
257.52

(0.7434)
(0.2437)

McLomax
2.7515
2.3568
0.9418
−0.1016
2.8189
262.90
275.93
268.17
263.54

(2.4395)
(0.2818)
(0.1849)
(0.8005)
(3.079)

BLomax
1.4533
2.2421
0.9361
0.6659

260.90
271.32
265.12
261.32

(8.1920)
(0.9818)
(0.1699)
(0.0624)

KwLomax
8.5753
2.0767
0.9188
−0.7243

260.90
271.32
265.12
261.32

(0.7854)
(7.8953)
(0.8728)
(7.1544)

ESLomax
1.5909

1.1135

258.50
263.70
260.60
258.62

(0.0860)

(0.6861)

LL
2.6536
0.3359

0.5196

258.96
266.77
262.12
259.21

(0.3217)
(0.23747)

(0.08362)

Table 4: MLEs (standard errors in parentheses) and the measures AIC, BIC, HQIC, and CAIC for simulated data from McLomax distribution with parameters , , ,  , and .

Model
Estimates

Measures

AIC
BIC
HQIC
CAIC

HLL
1.2207
2.4477

268.28
273.50
270.39
268.41

(2.0847)
(0.0177)

Lomax
1.4243
1.0314

267.49
272.70
269.60
267.61

(0.4009)
(0.4619)

McLomax
14.1535
3.2796
0.8091
−0.8197
2.9511
266.95
279.97
272.22
267.58

(0.1364)
(5.3498)
(0.9234)
(9.1029)
(4.0079)

BLomax
1.9585
3.7068
0.6586
0.3007

265.67
276.09
269.89
266.09

(6.0364)
(3.4102)
(0.1077)
(4.0353)

KwLomax
0.3924
7.7169
0.6896
6.1821

265.39
275.81
269.61
265.81

(0.6603)
(12.8494)
(0.0966)
(11.7374)

ESLomax
1.2764

0.8217

264.94
270.15
267.04
265.06

(0.1748)

(0.1031)

LL
8.8655
0.03389

0.0359

264.02
271.83
267.18
264.27

(15.7662)
(0.0695)

(0.0278)

Table 5: MLEs (standard errors in parentheses) and the measures AIC, BIC, HQIC, and CAIC for simulated data from BLomax distribution with parameters , , , and .

Model
Estimates

Measures

AIC
BIC
HQIC
CAIC

HLL
24.4487
0.0112

533.90
539.11
536.01
534.02

(13.7405)
(0.0065)

Lomax
23.6200
0.0081

544.04
549.25
546.15
544.17

(11.1025)
(0.0038)

McLomax
1.3135
18.6449
1.4939
9.3587
2.4534
534.96
547.99
540.23
535.60

(9.1629)
(73.1176)
(0.3102)
(78.4737)
(3.8777)

BLomax
2.0366
93.9001
1.7237
13.8059

533.49
543.91
537.71
533.91

(26.5471)
(159.3791)
(0.2569)
(194.682)

KwLomax
0.9258
35.7112
1.5236
21.0737

533.05
543.47
537.27
533.47

(2.386)
(17.316)
(0.228)
(139.842)

ESLomax
1.3483

5.0645

561.43
566.64
563.53
561.55

(0.1185)

(0.8554)

LL
10.6838
0.0392

16.8989

535.87
543.69
539.04
536.12

(5.5325)
(0.0261)

(15.5951)

Table 6: MLEs (standard errors in parentheses) and the measures AIC, BIC, HQIC, and CAIC for simulated data from KwLomax distribution with parameters , , , and .

Model
Estimates

Measures

AIC
BIC
HQIC
CAIC

HLL
3.2092
0.1872

454.88
460.09
456.99
455.02

(1.0615)
(0.0847)

Lomax
5.2637
0.0650

455.74
460.95
457.85
455.87

(3.0135)
(0.0436)

McLomax
36.3053
64.3913
1.4745
−0.6614
26.4402
455.68
468.70
460.95
456.32

(49.9841)
(87.0837)
(0.2732)
(0.1299)
(21.5904)

BLomax
20.4375
8.2358
1.6963
−0.8265

455.46
465.89
459.68
455.89

(18.192)
(1.3201)
(0.3709)
(0.0270)

KwLomax
1.4823
3.9164
1.5331
0.8043

455.73
466.15
459.95
456.15

(2.386)
(7.0316)
(0.3464)
(8.0144)

ESLomax
1.3936

2.6629

457.01
462.22
459.12
457.13

(0.1429)

(0.4120)

LL
3.9058
0.1236

2.3957

457.48
465.30
460.65
457.73

(2.9870)
(0.1943)

(4.9588)

Table 7: MLEs (standard errors in parentheses) and the measures AIC, BIC, HQIC, and CAIC for simulated data from ESLomax distribution with parameters and .

Model
Estimates

Measures

AIC
BIC
HQIC
CAIC

HLL
2.9230
1.4697

70.15
75.35
72.25
70.27

(0.8789)
(0.6175)

Lomax
4.4945
0.5392

71.43
76.64
73.54
71.55

(2.3241)
(0.3345)

McLomax
9.2546
0.8577
1.9039
−0.6952
2.5132
72.37
85.40
77.65
73.01

(18.1860)
(0.7067)
(0.9458)
(0.5267)
(3.9407)

BLomax
0.5806
0.4735
1.6416
3.3194

70.61
81.04
74.83
71.04

(1.7619)
(0.3488)
(0.4155)
(13.6281)

KwLomax
2.4388
0.5530
1.6260
−0.0069

70.64
81.07
74.86
71.07

(1.1386)
(0.6732)
(0.1428)
(7.9604)

ESLomax
3.4021

1.3948

67.33
72.54
69.44
67.46

(0.4127)

(0.1956)

LL
3.1584
1.2813

3.5394

72.86
80.68
76.03
73.12

(1.3691)
(1.4460)

(6.8140)

Table 8: MLEs (standard errors in parentheses) and the measures AIC, BIC, HQIC, and CAIC for simulated data from LL distribution with parameters , , and .

Model
Estimates

Measures

AIC
BIC
HQIC
CAIC

HLL
1.8358
1.2344

252.97
258.19
255.08
253.10

(0.4189)
(0.4553)

Lomax
2.1811
0.5669

253.22
258.43
255.33
253.34

(0.6717)
(0.2408)

McLomax
0.7992
1.2223
0.9887
1.6530
1.9169
259.10
272.12
264.37
259.73

(2.8691)
(1.7168)
(0.1874)
(10.1760)
(3.9962)

BLomax
1.6711
1.7124
1.0130
0.2907

257.21
267.63
261.43
257.63

(94.5364)
(1.0299)
(0.1929)
(73.1088)

KwLomax
8.6899
1.7774
0.9917
−0.7481

257.22
267.64
261.44
257.64

(7.6698)
(0.8502)
(0.2342)
(0.2298)

ESLomax
1.6418

1.1489

254.10
259.31
256.21
254.23

(0.2057)

(0.1530)

LL
1.9522
1.0375

3.1618

255.07
262.89
258.24
255.32

(0.5707)
(1.7363)

(11.1845)

The comparison is based upon the formal goodness-of-fit tests given above. The values of best fitted model are highlighted in the tables. Based on the goodness-of-fit tests, we conclude that the HLL distribution has better fit than the other competing models.

7. Concluding Remarks

In this paper, we introduce a two-parameter HLL distribution by compounding the half-logistic and the Lomax distributions. The shape of hazard function of the new compounding distribution can be monotonically decreasing or upside-down bathtub (unimodal). Some mathematical and statistical properties of the new model including moments and moment generating functions, mean deviations from mean and median, quantile function, mean residual life function, mode, median, order statistics, and entropies are studied. We estimate the model parameters by maximum likelihood and determined the observed information matrix. We present a simulation study to illustrate the performance of MLEs. The flexibility and potentiality of the proposed model are illustrated by means of a real data set. We hope that the HLL distribution may attract wider range of applications in areas such as engineering, survival and lifetime data, economics, meteorology, hydrology, and others.

Appendix

Elements of Observed Information Matrix

The elements of observed information matrix () are given by

Disclosure

The authors acknowledge that the abstract of the manuscript had been presented in a conference paper under the link http://archive.stat.uconn.edu/lida17/program.pdf.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Authors’ Contributions

The authors have equally contributed to this work. All authors read and approved the final manuscript.

References

K. S. Lomax, “Business failures: another example of the analysis of failure data,” Journal of the American Statistical Association, vol. 49, no. 268, pp. 847–852, 1954.

E.-H. A. Rady, W. A. Hassanein, and T. A. Elhaddad, “The power Lomax distribution with an application to bladder cancer data,” SpringerPlus, vol. 5, 1838 pages, 2016.

M. H. Tahir, G. M. Cordeiro, M. Mansoor, and M. Zubair, “The Weibull-Lomax distribution: properties and applications,” Hacettepe Journal of Mathematics and Statistics, vol. 44, no. 2, pp. 455–474, 2015.

I. B. Abdul-Moniem, “Recurrence relations for moments of lower generalized order statistics from exponentiated Lomax distribution and its characterization,” International Journal of Mathematical Archive, vol. 3, pp. 2144–2150, 2012.

S. A. Al-Awadhi and M. E. Ghitany, “Statistical properties of Poisson-Lomax distribution and its application to repeated accidents data,” Journal of Applied Statistical Science, vol. 10, no. 4, pp. 365–372, 2001.

A. Asgharzadeh, H. S. Bakouch, and L. Esmaeili, “Pareto Poisson-Lindley distribution with applications,” Journal of Applied Statistics, vol. 40, no. 8, pp. 1717–1734, 2013.

G. M. Cordeiro, E. M. Ortega, and B. z. Popović, “The gamma-Lomax distribution,” Journal of Statistical Computation and Simulation, vol. 85, no. 2, pp. 305–319, 2015.

M. E. Ghitany, F. A. Al-Awadhi, and L. A. Alkhalfan, “Marshall-Olkin extended Lomax distribution and its application to censored data,” Communications in Statistics—Theory and Methods, vol. 36, no. 9-12, pp. 1855–1866, 2007.

R. C. Gupta, M. E. Ghitany, and D. K. Al-Mutairi, “Estimation of reliability from Marshall-Olkin extended Lomax distributions,” Journal of Statistical Computation and Simulation, vol. 80, no. 7-8, pp. 937–947, 2010.

A. J. Lemonte and G. M. Cordeiro, “An extended Lomax distribution,” Statistics, vol. 47, no. 4, pp. 800–816, 2013.

B. Al-Zahrani and H. Sagor, “Statistical analysis of the Lomax-logarithmic distribution,” Journal of Statistical Computation and Simulation, vol. 85, no. 9, pp. 1883–1901, 2015.

A. H. El-Bassiouny, N. F. Abdo, and H. S. Shahen, “Exponential lomax distribution,” International Journal of Computer Applications, vol. 121, no. 13, pp. 24–29, 2015.

N. M. Kilany, “Weighted Lomax distribution,” SpringerPlus, vol. 5, no. 1, article no. 1862, 2016.

G. M. Cordeiro, M. Alizadeh, and P. R. D. Marinho, “The type I half-logistic family of distributions,” Journal of Statistical Computation and Simulation, vol. 86, no. 4, pp. 707–728, 2016.

R. E. Glaser, “Bathtub and related failure rate characterizations,” Journal of the American Statistical Association, vol. 75, no. 371, pp. 667–672, 1980.

I. S. Gradshteyn and I. M. Ryzhik, Tables of Integrals, Series, and Products, Academic Press, New York, NY, USA, 2007.

E. T. Lee and J. W. Wang, Statistical Methods for Survival Data Analysis, John Wiley and Sons, New York, NY, USA, 3rd edition, 2003.

R. C. Gupta, P. L. Gupta, and R. D. Gupta, “Modeling failure time data by Lehman alternatives,” Communications in Statistics—Theory and Methods, vol. 27, no. 4, pp. 887–904, 1998.

M. V. Aarset, “How to identify a bathtub hazard rate,” IEEE Transactions on Reliability, vol. 36, no. 1, pp. 106–108, 1987.

R Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Austria, 2015, https://www.R-project.org/.