The Open Statistics & Probability Journal




ISSN: 1876-5270 ― Volume 10, 2019
RESEARCH ARTICLE

Kumaraswamy Distribution and Random Extrema



Tomasz J. Kozubowski1, *, Krzysztof Podgórski2
1 Department of Mathematics & Statistics, University of Nevada, Reno, NV, USA
2 Department of Statistics, Lund University, Lund, Sweden

Abstract

Objective:

We provide a new stochastic representation for a Kumaraswamy random variable with arbitrary non-negative parameters. The representation is in terms of maxima and minima of independent distributed standard uniform components and extends a similar representation for integer-valued parameters.

Result:

The result is further extended for generalized classes of distributions obtained from a “base” distribution function Fviz.G(x) = H(F(x)), where H is the CDF of Kumaraswamy distribution.

Keywords: Distortion function, Distribution theory, Extremes, Kumaraswamy generalized distribution, Marshall-Olkin scheme, Proportional hazards transform, Quadratic transmutation map, Random extrema, Sibuya distribution.


Article Information


Identifiers and Pagination:

Year: 2018
Volume: 09
First Page: 18
Last Page: 25
Publisher Id: TOSPJ-9-18
DOI: 10.2174/1876527001809010017

Article History:

Received Date: 7/3/2018
Revision Received Date: 26/6/2018
Acceptance Date: 29/6/2018
Electronic publication date: 31/07/2018
Collection year: 2018

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© 2018 Kozubowski et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


* Address correspondence to this author at the Department of Mathematics & Statistics, University of Nevada, Reno, NV, USA; Tel: 7757846643; E-mail: tkozubow@unr.edu




1. INTRODUCTION

There is a growing literature on generalized distributions based on Kumaraswamy distribution [1G. Aryal, and Q. Zhang, "Characterizations of Kumaraswamy Laplace distribution with applications", Econ. Qual. Control, vol. 31, pp. 59-70.[http://dx.doi.org/10.1515/eqc-2016-0009] ]. They are obtained from a “base” distribution with the cumulative distribution function (CDF) F as the CDF G (a generalized version of F) viz.

(1)

where

(2)

is the CDF of the Kumaraswamy distribution with parameters α, β > 0. The latter from now is denoted by Kα,β. In terms of random variables, the relation (1) can be stated as

(3)

where Y~G and T~Kα,β. This generalization of F was proposed in Cordeiro and de Castro [2G.M. Cordeiro, and M. de Castro, "A new family of generalized distributions", J. Stat. Comput. Simul., vol. 81, pp. 883-898.[http://dx.doi.org/10.1080/00949650903530745] ], where the authors developed its basic properties and presented generalizations of normal, Weibull, gamma, Gumbel, and inverse Gaussian distributions. Many other generalized distributions following this scheme have been developed since then, including recent works of de Pascoa et al. [3M.A.R. de Pascoa, E.M.M. Ortega, and G.M. Cordeiro, "The Kumaraswamy generalized gamma distribution with application in survival analysis", Stat. Methodol., vol. 8, pp. 411-433.[http://dx.doi.org/10.1016/j.stamet.2011.04.001] ], Nadarajah et al. [4A. Di Crescenzo, "Some results on the proportional reversed hazards model", Stat. Probab. Lett., vol. 50, pp. 313-321.[http://dx.doi.org/10.1016/S0167-7152(00)00127-9] ], Mameli [5N. Eugene, C. Lee, and F. Famoye, "The beta-normal distribution and its applications", Commun. Stat. Theory Methods, vol. 31, pp. 497-512.[http://dx.doi.org/10.1081/STA-120003130] ] and Aryal and Zhang [6M.C. Jones, "Kumaraswamy distribution: A beta-type distribution with some tractability advantages", Statist. Method., vol. 6, pp. 70-81.[http://dx.doi.org/10.1016/j.stamet.2008.04.001] ]. However, papers on the topic focus mainly on rather elementary properties and do not provide any significant theoretical interpretation of the construction. One exception is an interpretation for integer-valued α and β through maxima and minima of independent and identically distributed (IID) random components (e.g., Jones [7T.J. Kozubowski, and K. Podgórski, "Certain bivariate distributions and random processes connected with maxima and minima", Working Papers in Statistics 2016:9, Department of Statistics, School of Economics and Management, Lund University, 2016a. [Published online in Extremes, 2018, DOI 10.1007/s10687-018-0311-2].], Nadarajah et al. [4A. Di Crescenzo, "Some results on the proportional reversed hazards model", Stat. Probab. Lett., vol. 50, pp. 313-321.[http://dx.doi.org/10.1016/S0167-7152(00)00127-9] ], Nadarajah and Eljabri [8T.J. Kozubowski, and K. Podgórski, "Transmuted distributions and random extrema", Stat. Probab. Lett., vol. 116, pp. 6-8.[http://dx.doi.org/10.1016/j.spl.2016.04.001] ]). Indeed, assuming that α = m, β = n are positive integers, we find that xm is the CDF of the maximum of m IID standard uniform variables, with the corresponding survivor function (SF) being 1 - xm. Thus, the quantity (1 - xm)n in (2) is the SF of the minimum of n such random variables, with H being the corresponding CDF. In other words, we have the following stochastic representation of T~Km,n,

(4)

where {Ui,j} are IID standard uniform variables. This property, discussed in Jones [7T.J. Kozubowski, and K. Podgórski, "Certain bivariate distributions and random processes connected with maxima and minima", Working Papers in Statistics 2016:9, Department of Statistics, School of Economics and Management, Lund University, 2016a. [Published online in Extremes, 2018, DOI 10.1007/s10687-018-0311-2].], motivated the name minimax for this distribution. Below we extend this interpretation to the general Kumaraswamy distribution as well as to its generalization (1).

2. MAIN RESULTS

To obtain a representation of a Kumaraswamy random variable in terms of min/max, we shall use the following basic result (e.g., Kozubowski and Podgórski, [9P. Kumaraswamy, "A generalized probability density function for double bounded random processes", J. Hydrol., vol. 46, pp. 79-88.[http://dx.doi.org/10.1016/0022-1694(80)90036-0] ]), which relates the distributions of min/max of IID components with random number of terms to the relevant Probability Generating Function (PGF).

Lemma 2.1. If N is an random variable, independent of the sequence {Xi} of IID random variables with the CDF F, then the CDFs of the random variables

(5)

are given by FX(x) = GN(F(x)) and FY(y) = 1 - GN(1 - F(y)) , respectively, where GN is the PGF of N.

A notable application of this result, discussed in Kozubowski and Podgórski [9P. Kumaraswamy, "A generalized probability density function for double bounded random processes", J. Hydrol., vol. 46, pp. 79-88.[http://dx.doi.org/10.1016/0022-1694(80)90036-0] ], concerns a special case where the variable N = Nα in (5) has the Sibuya distribution (Sibuya, [10A.J. Lemonte, W. Barreto-Souza, and G.M. Cordeiro, "The exponentiated Kumaraswamy distribution and its log-transform", Braz. J. Probab. Stat., vol. 27, pp. 31-53.[http://dx.doi.org/10.1214/11-BJPS149] ]), given by the PGF

(6)

and the Probability Mass Function (PMF)

(7)

where necessarily , with the boundary case α = 1 corresponding to a unit mass at n = 1. This variable represents the number of trials till the first success in an infinite sequence of independent Bernoulli trials, where the probability of success varies with the trial, and for the nth trial equals α/n. Here, because of the special form of the PGF of the Sibuya random variable Nα, if the latter represents the random number of terms in Lemma 2.1, the CDFs of the random variables X and Y in (5) are given by FX(x) = 1 - [1 - F(x)]α and FY(Y) = [F(x)]α, respectively. This leads to our first result for the Kumaraswamy distribution with the parameters restricted to the unit interval (0, 1).

Proposition 2.1. Assume that {Ui,j} are IID standard uniform variables, have Sibuya distributions with respective parameters , and all the variables on the right-hand-side of (8) are mutually independent. Then the variable

(8)

has the Kumaraswamy distribution Kα, β.

The above result can be re-formulated for any Kumaraswamy generalized random variable obtained viz. (3), providing a meaningful interpretation of this construction in terms of maxima and minima of IID components with the “parent” CDF F.

Proposition 2.2. Let Nα(i), be IID Sibuya variables with parameter , and independent of another Sibuya variable Nβ, with parameter . Further, for a given CDF F, let Y be a random variable with the CDF G defined by (1), where H = Kα, β ; with . Then we have

(9)

where the {Xi,j} are IID random variables with CDF F, independent of Nα(i) and Nβ.

Remark 2.1. Let Bα, β denote beta distribution given by the PDF

(10)

Then, if either α = 1 or β = 1, the Kumaraswamy distribution Kα, β coincides with Bα, β. Thus, Proposition 2.1 specialized to this case, leads to stochastic representations of such beta-distributed random variables in terms of maxima and minima, i.e. if for the {Ui} are IID standard uniform random variables, independent of Sibuya-distributed Nα and Nβ with we define

(11)

then B1~Bα, 1 and B2~B1, β. Further, if either α = 1 or β = 1, the generalized distributions obtained viz. (1) are special cases of beta-generalized distributions popularized by Eugene et al. [11V. Mameli, "Kumaraswamy skew-normal distribution", Stat. Probab. Lett., vol. 104, pp. 75-81.[http://dx.doi.org/10.1016/j.spl.2015.04.031] ], arising when H in (1) is the CDF corresponding to (10). Thus, Proposition 2.2 specialized to this case shows that if a random variables Y is defined viz. (3), where either T~Bα, 1 or T~B1, β with , then we have

(12)

respectively, where the {Xi} are IID random variables with CDF F, independent of Sibuya-distributed Nα and Nβ.

An extension of Proposition 2.1 to the case where the parameters are no longer restricted to the unit interval is straightforward. To formulate the result, we shall split a positive number r into r = {r} + < r >, where

(13)

We shall also need the following result taken from Kozubowski and Podgórski [9P. Kumaraswamy, "A generalized probability density function for double bounded random processes", J. Hydrol., vol. 46, pp. 79-88.[http://dx.doi.org/10.1016/0022-1694(80)90036-0] ], where we use the standard convention that the min and max over an empty set are understood as and -∞, respectively.

Proposition 2.3. If X and Y have CDFs given by 1 - (1 - F(x))r and [F(x)]r, respectively, where F is a CDF and , then they admit the stochastic representations

(14)

where N< r > has the Sibuya distribution (7) with parameter α = < r > and is independent of the IID {Xj} with the CDF F.

When we set r = β and apply the first representation in (14) to a Kumaraswamy random variable T~Kα, β, , we obtain

(15)

where N< β > has Sibuya distribution with parameter < β > and {Xj} is IID with the CDF xα. In turn, when we set r = α and apply the second representation in (14) to each Xj in (15), we obtain

(16)

where the {Ui,j} are IID standard uniform variables and the N< α >(j) are IID and Sibuya distributed with parameter < α >, independent of the {Ui,j}. By combining (15) with (16), we obtain the following result.

Proposition 2.4. Let T~Kα, β with general α, β > 0. Then we have

(17)

where the {Ui,j} are IID standard uniform variables, N< α >(j) and N< β > have Sibuya distributions with respective parameters < α > and < β >, and all the variables on the right-hand-side of (17) are mutually independent.

Remark 2.2. If the parameter α of T~Kα, β is a positive integer, , then we have {α} = m - 1, < α > = 1, so that the Sibuya variables N< α >(j) are equal to 1 almost surely. Thus, in the same notation, the representation (17) turns into

(18)

On the other hand, if the parameter β of T~Kα, β is a positive integer, , then we have {β} = n - 1, < β > = 1, and the Sibuya variable N< β > is equal to 1 almost surely, in which case the representation (17) reduces to

(19)

Moreover, if both parameters of T~Kα, β are positive integers, so that in (18) we have and in (19) we have , then both, (18) and (19), turn into the representation (4) derived by Jones [7T.J. Kozubowski, and K. Podgórski, "Certain bivariate distributions and random processes connected with maxima and minima", Working Papers in Statistics 2016:9, Department of Statistics, School of Economics and Management, Lund University, 2016a. [Published online in Extremes, 2018, DOI 10.1007/s10687-018-0311-2].].

Remark 2.3. The stochastic representation (17) can be extended to the case of exponentiated Kumaraswamy (EK) distribution studied by Lemonte et al. [12A.W. Marshall, and I. Olkin, A new method for adding a parameter to a family of distributions with application to the exponential and Weibull families., vol. 84, Biometrika, .], which is given by the CDF

(20)

This can be achieved by another application of Proposition 2.3 on top of Proposition 2.4, leading to

(21)

where W has EK distribution (20), γ = {γ} + < γ > with , and N< γ >has Sibuya distribution (7) with parameter α = < γ >, and is independent of the IID Kumaraswamy random variables {Tj} with the CDF (2). In turn, each of the Kumaraswamy variables Tj admits the stochastic representation (17) involving minima and maxima of IID standard uniform variables.

Finally, we extend Proposition 2.4 to generalized random variables obtained from Kumaraswamy distribution viz. (3), which links this construction with maxima and minima of IID components with the “parent” CDF F.

Proposition 2.5. Let Y be a random variable with the CDF G defined by (1), where H = Kα, β with α, β > 0. Then we have

(22)

where the {Xi,j} are IID random variables with CDF F, N< α >(j) and N< β > have Sibuya distributions with respective parameters < α > and < β >, and all variables on the right-hand-side of (22) are mutually independent.

Let us consider again the stochastic representation of T~Km, n given by (4). As discussed in Nadarajah et al. [4A. Di Crescenzo, "Some results on the proportional reversed hazards model", Stat. Probab. Lett., vol. 50, pp. 313-321.[http://dx.doi.org/10.1016/S0167-7152(00)00127-9] ] (Nadarajah and Eljabri, [8T.J. Kozubowski, and K. Podgórski, "Transmuted distributions and random extrema", Stat. Probab. Lett., vol. 116, pp. 6-8.[http://dx.doi.org/10.1016/j.spl.2016.04.001] ]), T can be thought of as a lifetime of a system consisting of n sub-systems connected in a series, where each sub-system is composed of m IID components with standard uniform lifetimes. If the lifetimes are non-negative and IID with the CDF F, then similar interpretation holds for the random variable Y obtained viz. (3) with T~Km, n. The results presented above provide a similar interpretation for a general T~Kα, β as well as for a non-negative variable Y obtained viz. (3) with T~Kα, β, where the number of IID components is a random variable.

In connection with this interpretation of (4), one can instead assume that the n sub-systems are connected in parallel rather than in a series, while the IID standard uniform components {Ui,j} within each sub-system are connected in a series. In this case, the total lifetime of the system will admit the representation

(23)

It is easy to see that in (23) has the same distribution as 1 - T, where T~Km, n. More generally, let us consider the random variable , where T~Kα, β, which we shall call a dual of the latter, and denote its distribution by . Clearly,

(24)

Remark 2.4. Interestingly, the class of distributions on the unit interval defined viz. (24) coincides with the class of distributions whose CDFs are given by the quantile functions corresponding to the Kumaraswamy CDF (2).

All the results for the Kumaraswamy and Kumaraswamy-generated distributions presented above have analogs in terms of the dual distributions (24) and the generalizations of F obtained viz.

(25)

It turns out that they are obtained by swapping the minima with the maxima in the results for the Kumaraswamy and Kumaraswamy-generated distributions. We shall state them, without proofs, for distributions (25) since other results are obtained by setting F (x) = x. As before, we start with the case .

Proposition 2.6. Let F be a random variable with the CDF G defined by (25) with have Sibuya distributions with respective parameters and . Then

(26)

where the {Xi,j} are IID random variables with CDF F, independent of the Sibuya variables.

An extension to the general case presented below can be derived from Proposition 2.3.

Proposition 2.7. Let Y be a random variable with the CDF G defined by (25) with α , β > 0. Then

(27)

where the {Xi,j} are IID random variables with CDF F, independent of two independent Sibuya variables N< α >(j) and N< β > with parameters < α > and < β >, respectively.

Remark 2.5. If Y in Propositions 2.6 and 2.7 has a dual Kumaraswamy distribution with the CDF (24), then the stochastic representations still apply with the {Xi,j} being IID standard uniform variables.

Remark 2.6. If the underlying variables are non-negative, our results have an interpretation in terms of series-parallel or parallel-series systems of IID components where the number of components can be random (and Sibuya distributed). For example, the quantity Y in Proposition 2.2 represents the lifetime of ra andom number Nβ of sub-systems connected in parallel, where each sub-system i consists of a random number Nα(i) of components connected in a series. Similarly, the quantity Y in Proposition 2.6 represents the lifetime of Nβ sub-systems connected in a series, where each sub-system consists of a random number of components connected in parallel. The other results have analogous, albeit not as straightforward, interpretations.

Remark 2.7. As discussed above, the stochastic representations for Kumaraswamy and related distributions are built on min/max interpretations of distributions given by the survival function or the CDF Fr with non-negative r, where F is a “base” CDF. These two transformations are known as the proportional hazard and the proportional reversed hazard transforms (or models, see, e.g., Wang [13S. Nadarajah, G.M. Cordeiro, and E.M.M. Ortega, "General results for the Kumaraswamy-G distribution", J. Stat. Comput. Simul., vol. 82, pp. 951-979.[http://dx.doi.org/10.1080/00949655.2011.562504] ] and Di Crescenzo [14S. Nadarajah, and S. Eljabri, "The Kumaraswamy GP distribution", J. Data Sci., vol. 11, pp. 739-766.]), since the hazard rates of distributions given by F and are proportional (as are the the reversed hazard rates of distributions given by F and Fr). It also is worth noting that these two generalizations of F are examples of the so-called distorted distributions, which in general are obtained through a transformation q(F) where q is a continuous and increasing bijective function on the unit interval onto itself (see, e.g., Navarro et al. [15M. Sibuya, "Generalized hypergeometric, digamma, and trigamma ditributions", Ann. Inst. Stat. Math., vol. 31, pp. 373-390.[http://dx.doi.org/10.1007/BF02480295] ], Navarro [16J. Navarro, "Stochastic comparisons of generalized mixtures and coherent systems", Test, vol. 25, pp. 150-169.[http://dx.doi.org/10.1007/s11749-015-0443-5] ], Navarro and Gomis [18J. Navarro, and M.C. Gomis, "Comparisons in the mean residual life order of coherent systems with identically distributed components", Appl. Stochastic Models Bus. Industry, vol. 32, no. 1, pp. 33-47.[http://dx.doi.org/10.1002/asmb.2121] ] and references therein). Consequently, using this connection and the results for distorted distributions one can study preservation of reliability aging classes and related properties for Kumaraswamy-transformed distributions obtained viz. (1) or (25).

By means of Lemma 2.1, several other classes of generalized distributions based on a “parent” CDF F, which were introduced in recent years, can also be shown to arise from random maxima and minima of IID components with distribution F. For example, as shown by Kozubowski and Podgórski [18J. Navarro, and M.C. Gomis, "Comparisons in the mean residual life order of coherent systems with identically distributed components", Appl. Stochastic Models Bus. Industry, vol. 32, no. 1, pp. 33-47.[http://dx.doi.org/10.1002/asmb.2121] ], if N has a Bernoulli distribution with parameter shifted up by one, so that its PGF is

(28)

we obtain the so-called transmuted version of F, defined by

(29)

Another example is provided by the so-called Marschall-Olkin generalized-F distributions, which were made popular by Marshall and Olkin [19F.J. Samaniego, System Signatures and their Applications in Engineering Reliability., Springer: New York, .[http://dx.doi.org/10.1007/978-0-387-71797-5] ] who introduced them through minima and maxima of IID components (following distribution F) with a geometric number of terms N.

Remark 2.8. The CDF (29) is a generalized mixture of F and F2 (where the weights are allowed to be negative, see, e.g., Navarro, [16J. Navarro, "Stochastic comparisons of generalized mixtures and coherent systems", Test, vol. 25, pp. 150-169.[http://dx.doi.org/10.1007/s11749-015-0443-5] ]) as well as usual mixture of F2 and 1 - (1 - F)2 (with non-negative weights (1 - α)/2 and (1 + α)/2, respectively). The latter representation as a mixed system (e.g., Samaniego, [20S. Wang, "Insurance pricing and increased limits ratemaking by proportional hazards transforms", Insur. Math. Econ., vol. 17, pp. 43-54.[http://dx.doi.org/10.1016/0167-6687(95)00010-P] ]) allows an alternative interpretation of (29) in terms of lifetimes of series/parallel systems with IID components.

CONSENT FOR PUBLICATION

Not applicable.

CONFLICT OF INTEREST

The authors declare no conflict of interest, financial or otherwise.

ACKNOWLEDGEMENTS

We thank two referees for their remarks and additional references. Kozubowski’s research was partially supported by the European Union’s Seventh Framework Programme for Research, Technological Development and Demonstration under grant agreement no 318984 - RARE. Podgórski’s research was supported by Swedish Research Council Grant Dnr: 2013-5180.

REFERENCES

[1] G. Aryal, and Q. Zhang, "Characterizations of Kumaraswamy Laplace distribution with applications", Econ. Qual. Control, vol. 31, pp. 59-70.[http://dx.doi.org/10.1515/eqc-2016-0009]
[2] G.M. Cordeiro, and M. de Castro, "A new family of generalized distributions", J. Stat. Comput. Simul., vol. 81, pp. 883-898.[http://dx.doi.org/10.1080/00949650903530745]
[3] M.A.R. de Pascoa, E.M.M. Ortega, and G.M. Cordeiro, "The Kumaraswamy generalized gamma distribution with application in survival analysis", Stat. Methodol., vol. 8, pp. 411-433.[http://dx.doi.org/10.1016/j.stamet.2011.04.001]
[4] A. Di Crescenzo, "Some results on the proportional reversed hazards model", Stat. Probab. Lett., vol. 50, pp. 313-321.[http://dx.doi.org/10.1016/S0167-7152(00)00127-9]
[5] N. Eugene, C. Lee, and F. Famoye, "The beta-normal distribution and its applications", Commun. Stat. Theory Methods, vol. 31, pp. 497-512.[http://dx.doi.org/10.1081/STA-120003130]
[6] M.C. Jones, "Kumaraswamy distribution: A beta-type distribution with some tractability advantages", Statist. Method., vol. 6, pp. 70-81.[http://dx.doi.org/10.1016/j.stamet.2008.04.001]
[7] T.J. Kozubowski, and K. Podgórski, "Certain bivariate distributions and random processes connected with maxima and minima", Working Papers in Statistics 2016:9, Department of Statistics, School of Economics and Management, Lund University, 2016a. [Published online in Extremes, 2018, DOI 10.1007/s10687-018-0311-2].
[8] T.J. Kozubowski, and K. Podgórski, "Transmuted distributions and random extrema", Stat. Probab. Lett., vol. 116, pp. 6-8.[http://dx.doi.org/10.1016/j.spl.2016.04.001]
[9] P. Kumaraswamy, "A generalized probability density function for double bounded random processes", J. Hydrol., vol. 46, pp. 79-88.[http://dx.doi.org/10.1016/0022-1694(80)90036-0]
[10] A.J. Lemonte, W. Barreto-Souza, and G.M. Cordeiro, "The exponentiated Kumaraswamy distribution and its log-transform", Braz. J. Probab. Stat., vol. 27, pp. 31-53.[http://dx.doi.org/10.1214/11-BJPS149]
[11] V. Mameli, "Kumaraswamy skew-normal distribution", Stat. Probab. Lett., vol. 104, pp. 75-81.[http://dx.doi.org/10.1016/j.spl.2015.04.031]
[12] A.W. Marshall, and I. Olkin, A new method for adding a parameter to a family of distributions with application to the exponential and Weibull families., vol. 84, Biometrika, .
[13] S. Nadarajah, G.M. Cordeiro, and E.M.M. Ortega, "General results for the Kumaraswamy-G distribution", J. Stat. Comput. Simul., vol. 82, pp. 951-979.[http://dx.doi.org/10.1080/00949655.2011.562504]
[14] S. Nadarajah, and S. Eljabri, "The Kumaraswamy GP distribution", J. Data Sci., vol. 11, pp. 739-766.
[15] M. Sibuya, "Generalized hypergeometric, digamma, and trigamma ditributions", Ann. Inst. Stat. Math., vol. 31, pp. 373-390.[http://dx.doi.org/10.1007/BF02480295]
[16] J. Navarro, "Stochastic comparisons of generalized mixtures and coherent systems", Test, vol. 25, pp. 150-169.[http://dx.doi.org/10.1007/s11749-015-0443-5]
[17] J. Navarro, Y. del Águila, M.A. Sordo, and A. Suárez-Llorens, "Preservation of reliability classes under the formation of coherent systems", Appl. Stochastic Models Bus. Industry, vol. 30, pp. 444-454.[http://dx.doi.org/10.1002/asmb.1985]
[18] J. Navarro, and M.C. Gomis, "Comparisons in the mean residual life order of coherent systems with identically distributed components", Appl. Stochastic Models Bus. Industry, vol. 32, no. 1, pp. 33-47.[http://dx.doi.org/10.1002/asmb.2121]
[19] F.J. Samaniego, System Signatures and their Applications in Engineering Reliability., Springer: New York, .[http://dx.doi.org/10.1007/978-0-387-71797-5]
[20] S. Wang, "Insurance pricing and increased limits ratemaking by proportional hazards transforms", Insur. Math. Econ., vol. 17, pp. 43-54.[http://dx.doi.org/10.1016/0167-6687(95)00010-P]

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Debomoy K. Lahiri
(Indiana University School of Medicine, USA)

"Open access journals represent a major break-through in publishing. They provide easy access to the latest research on a wide variety of issues. Relevant and timely articles are made available in a fraction of the time taken by more conventional publishers. Articles are of uniformly high quality and written by the world's leading authorities."


Robert Looney
(Naval Postgraduate School, USA)

"Open access journals have transformed the way scientific data is published and disseminated: particularly, whilst ensuring a high quality standard and transparency in the editorial process, they have increased the access to the scientific literature by those researchers that have limited library support or that are working on small budgets."


Richard Reithinger
(Westat, USA)

"Not only do open access journals greatly improve the access to high quality information for scientists in the developing world, it also provides extra exposure for our papers."


J. Ferwerda
(University of Oxford, UK)

"Open Access 'Chemistry' Journals allow the dissemination of knowledge at your finger tips without paying for the scientific content."


Sean L. Kitson
(Almac Sciences, Northern Ireland)

"In principle, all scientific journals should have open access, as should be science itself. Open access journals are very helpful for students, researchers and the general public including people from institutions which do not have library or cannot afford to subscribe scientific journals. The articles are high standard and cover a wide area."


Hubert Wolterbeek
(Delft University of Technology, The Netherlands)

"The widest possible diffusion of information is critical for the advancement of science. In this perspective, open access journals are instrumental in fostering researches and achievements."


Alessandro Laviano
(Sapienza - University of Rome, Italy)

"Open access journals are very useful for all scientists as they can have quick information in the different fields of science."


Philippe Hernigou
(Paris University, France)

"There are many scientists who can not afford the rather expensive subscriptions to scientific journals. Open access journals offer a good alternative for free access to good quality scientific information."


Fidel Toldrá
(Instituto de Agroquimica y Tecnologia de Alimentos, Spain)

"Open access journals have become a fundamental tool for students, researchers, patients and the general public. Many people from institutions which do not have library or cannot afford to subscribe scientific journals benefit of them on a daily basis. The articles are among the best and cover most scientific areas."


M. Bendandi
(University Clinic of Navarre, Spain)

"These journals provide researchers with a platform for rapid, open access scientific communication. The articles are of high quality and broad scope."


Peter Chiba
(University of Vienna, Austria)

"Open access journals are probably one of the most important contributions to promote and diffuse science worldwide."


Jaime Sampaio
(University of Trás-os-Montes e Alto Douro, Portugal)

"Open access journals make up a new and rather revolutionary way to scientific publication. This option opens several quite interesting possibilities to disseminate openly and freely new knowledge and even to facilitate interpersonal communication among scientists."


Eduardo A. Castro
(INIFTA, Argentina)

"Open access journals are freely available online throughout the world, for you to read, download, copy, distribute, and use. The articles published in the open access journals are high quality and cover a wide range of fields."


Kenji Hashimoto
(Chiba University, Japan)

"Open Access journals offer an innovative and efficient way of publication for academics and professionals in a wide range of disciplines. The papers published are of high quality after rigorous peer review and they are Indexed in: major international databases. I read Open Access journals to keep abreast of the recent development in my field of study."


Daniel Shek
(Chinese University of Hong Kong, Hong Kong)

"It is a modern trend for publishers to establish open access journals. Researchers, faculty members, and students will be greatly benefited by the new journals of Bentham Science Publishers Ltd. in this category."


Jih Ru Hwu
(National Central University, Taiwan)


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