Modeling failure time data by lehman alternatives

Gupta, Ramesh C.; Gupta, Pushpa L.; Gupta, Rajeev

doi:10.1080/03610929808832134

Cited by 679 publications

(435 citation statements)

References 10 publications

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“…The properties of Exp-G distributions have been studied by many authors in recent years, see Mudholkar and Srivastava (1993) and Mudholkar, Srivastava, and Freimer (1995) for exponentiated Weibull, Gupta, Gupta, and Gupta (1998) for exponentiated Pareto, Gupta and Kundu (1999) for exponentiated exponential, Nadarajah (2005) for exponentiated Gumbel, Shirke and Kakade (2006) for exponentiated log-normal and Nadarajah and Gupta (2007) for exponentiated gamma distributions.…”

Section: Useful Expansionsmentioning

confidence: 99%

The Odd Lindley-G Family of Distributions

Gomes-Silva¹,

Percontini²,

Brito³

et al. 2017

AJS

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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, reliability, order statistics and their moments and k upper record values. We provide a Monte Carlo simulation study to evaluate the maximum likelihood estimates. We discuss estimation of the model parameters by maximum likelihood and provide an application to a real data set.

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Section: Useful Expansionsmentioning

confidence: 99%

The Odd Lindley-G Family of Distributions

Gomes-Silva¹,

Percontini²,

Brito³

et al. 2017

AJS

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“…The mathematical properties of the exp-R model were studied by several authors, e.g. Mudholkar et al(1995), Gupta et al(1998), Gupta and Kundu (1999) and Nadarajah and Kotz (2006).…”

Section: Asymptotic and Shapesmentioning

confidence: 99%

On Marshall-Olkin Burr X family of distribution

Jamal¹,

Tahir²,

Alizadeh³

et al. 2017

Tbilisi Math. J.

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Generalizing distributions is important for applied statisticians and recent literature has suggested several ways of extending well-known distributions. We propose a new class of distributions called the Marshall-Olkin Burr X family, which yields flexible shapes for its density such as symmetrical, left-skewed, right-skewed and reversed-J shaped, and can have increasing, decreasing,constant, bathtub and upside-down bathtub hazard rates shaped. Some of its structural properties including quantile and generating functions, ordinary and incomplete moments, and mean deviations are obtained. One special model of this family, the MarshallOlkin-Burr-Lomax distribution, is investigated in details. We also derive the density of the order statistics. The model parameters are estimated by the maximum likelihood method. For illustrative purposes, three applications to real life data are presented.

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“…Following this idea, Gupta et al (1998) introduced the exponentiated exponential distribution as a generalization of the exponential distribution. In the same way, Nadarajah and Kotz (2006) proposed four more exponentiated distributions which generalize the gamma, Weibull, Gumbel and Fréchet distributions and provided some mathematical properties for each distribution.…”

Section: Introductionmentioning

confidence: 99%

Extended generalized extreme value distribution with applications in environmental data

Nascimento¹,

Bourguignon²,

Leão³

2015

HJMS

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In probability theory and statistics, the generalized extreme value (GEV) distribution is a family of continuous probability distributions developed within extreme value theory, which has wide applicability in several areas including hydrology, engineering, science, ecology and finance. In this paper, we propose three extensions of the GEV distribution that incorporate an additional parameter. These extensions are more flexible than the GEV distribution, i.e., the additional parameter introduces skewness and to vary tail weight. In these three cases, the GEV distribution is a particular case. The parameter estimation of these new distributions is done under the Bayesian paradigm, considering vague priors for the parameters. Simulation studies show the efficiency of the proposed models. Applications to river quotas and rainfall show that the generalizations can produce more efficient results than is the standard case with GEV distribution.

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Modeling failure time data by lehman alternatives

Cited by 679 publications

References 10 publications

The Odd Lindley-G Family of Distributions

The Odd Lindley-G Family of Distributions

On Marshall-Olkin Burr X family of distribution

Extended generalized extreme value distribution with applications in environmental data

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