2017
DOI: 10.17713/ajs.v46i1.222
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The Odd Lindley-G Family of Distributions

Abstract: We propose a new generator of continuous distributions with one extra positive parameter called the odd Lindley-G family. Some special cases are presented. The new density function can be expressed as a linear combination of exponentiated densities based on the same baseline distribution. Various structural properties of the new family, which hold for any baseline model, are derived including explicit expressions for the quantile function, ordinary and incomplete moments, generating function, Rényi entropy, re… Show more

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Cited by 97 publications
(61 citation statements)
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“…The data corresponding to lifetimes (in years) of retired women with temporary disabilities who died during 2004 and which are incorporated in the Mexican insurance public system are: 22…”
Section: Fig 8 a Graphical Summary Of Dataset IImentioning
confidence: 99%
“…The data corresponding to lifetimes (in years) of retired women with temporary disabilities who died during 2004 and which are incorporated in the Mexican insurance public system are: 22…”
Section: Fig 8 a Graphical Summary Of Dataset IImentioning
confidence: 99%
“…There are several standard probability distributions that have been used over the years for modelling real-life datasets however research has shown that most of these distributions do not adequately model some of these heavily skewed datasets and therefore creating a problem in statistical theory and applications. Recently, numerous extended or compound probability distributions have proposed in the literature for modeling real-life situations and these compound distributions are found to be skewed, flexible and more better in statistical modeling compared to their standard counterparts [1][2][3][4][5][6][7][8][9][10][11][12][13].…”
Section: Introductionmentioning
confidence: 99%
“…New families of distributions are produced day by day and are useful for adding parameters to all forms of probability distributions which makes the resulting distribution more flexible for modeling heavily skewed dataset. Some of these families of distributions include the beta generated family (Beta-G) [7], Transmuted family of distributions [8], Gamma-G (type 1) [9], the Kumaraswamy-G family [10], McDonald-G family [11], Gamma-G (type 2) family [12], Gamma-G (type 3) family [13], Log-gamma-G family [14], Exponentiated T-X family [15], Exponentiated-G (EG) family [16], Weibull-X family [17], Weibull-G family [18], Logistic-G family [19], Gamma-X family [20], a Lomax-G family [21], a new generalized Weibull-G family [22], a Beta Marshall-Olkin family of distributions [23], Logistic-X family [24], a new Weibull-G family [25], a Lindley-G family [26], a Gompertz-G family [27] and Odd Lindley-G family [28] and so on.…”
Section: Introductionmentioning
confidence: 99%