A Marshall-Olkin Power Log-normal Distribution and Its Applications to Survival Data

Gui, Wenhao

doi:10.5539/ijsp.v2n1p63

Cited by 19 publications

(10 citation statements)

References 20 publications

(14 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is because (3) is very small for large values of α if x ≥ 2. Figure 3 shows the behaviour of the MOEZipf distribution in the log-log scale, for different parameter values, together with the straight line obtained by changing by = in (7).…”

Section: The Marshall-olkin Extended Zipf Distributionmentioning

confidence: 99%

See 1 more Smart Citation

Using the Marshall-Olkin Extended Zipf Distribution in Graph Generation

Duarte-López

Prat-Pérez

Pérez-Casany

2015

Euro-Par 2015: Parallel Processing Workshops

View full text Add to dashboard Cite

The Zipf distribution also known as scale-free distribution or discrete Pareto distribution, is the particular case of Power Law distribution with support the strictly positive integers. It is a one-parameter distribution with a linear behaviour in the log-log scale. In this paper the Zipfian distribution is generalized by means of the Marshall-Olkin transformation. The new model has more flexibility to adjust the probabilities of the first positive integer numbers while keeping the linearity of the tail probabilities. The main properties of the new model are presented, and several data sets are analyzed in order to show the gain obtained by using the generalized model.

show abstract

Section: The Marshall-olkin Extended Zipf Distributionmentioning

confidence: 99%

“…Several papers that appear in the last few years apply the generalizations in reliability, in time series and in censored data. See for instance [1], [5,6] or [7]. In [11] that transformation is presented as a skewing mechanism, and several classes of unimodal and symmetric distributions are extended in that manner.…”

Section: Introductionmentioning

confidence: 99%

Using the Marshall-Olkin Extended Zipf Distribution in Graph Generation

Duarte-López

Prat-Pérez

Pérez-Casany

2015

Euro-Par 2015: Parallel Processing Workshops

View full text Add to dashboard Cite

show abstract

“…Some statistical properties of these new distributions were illustrated. For example, Marshall-Olkin logistic processes has been introduced by (Alice and Jose 2005), (Gui 2013 (Ghitany et al 2007) introduced Marshall-Olkin Extended Lomax distribution. (Marshall and Olkin 1997) have added a parameter to the base line cdf in exponential and Weibull families as follows 3This study aims at providing a new extension of weibull distribution namely, The Marshall-Olkin Generalized Inverse Weibull (MOGIW for short) distribution.…”

Section: Introductionmentioning

confidence: 99%

The Marshall–Olkin Generalized Inverse Weibull Distribution: Properties and Application

Salem¹

2019

MAS

View full text Add to dashboard Cite

In this paper, a new distribution namely, The Marshall–OlkinGeneralized Inverse Weibull Distribution is illustrated and studied. The new distribution is very flexible and contains sub-models such asinverse exponential, inverse Rayleigh, Weibull, inverse Weibull, Marshall–Olkininverse Weibull and Fréchetdistributions. Also, the hazard function of the new distribution can produce variety of forms:an increase, a decrease and an upside-down bathtub. Some properties such as hazard function, quintile function, entropy, moment generating function and order statistics are obtained. Different estimation approaches namely, maximum likelihood estimators, interval estimators, least square estimators, fisher information matrix and asymptotic confidence intervals are described. To illustrate the superior performance of the proposed distribution, a simulation study and a real data analysis are investigated against other models.

show abstract

“…Several studies have been conducted to propose several distribution extensions and discussed their properties and inference. These includes the Marshall-Olkin extended (MOE) Weibull proposed by Ghitany et al [9], MOE Pareto proposed by Ghitany [7], MOE gamma proposed by Ristic et al [15], MOE Lomax using censored data proposed by Ghitany et al [8], MOE exponential distribution proposed by Srivastava et al [19], MOE Uniform distribution proposed by Jose and Krishna [12], both MOE power log-normal and MOE log-logistic distributions proposed by Gui [10], [11], and MOE Burr type XII distribution proposed by Al-Saiari et al [1]. In addition, Santos-Neto et al [16] introduced the MOE Weibull family of distributions and studied some of its mathematical properties.…”

Section: Introductionmentioning

confidence: 99%

Marshall-Olkin extended Burr III distribution

Al-Saiari¹,

Mousa²,

Baharith³

2016

imf

View full text Add to dashboard Cite

In this paper, a new extended Burr III distribution using Marshall and Olkin method is introduced. Some characteristics and properties of the new distribution are studied. Maximum likelihood and Bayesian methods are used to estimate model parameters. Also, the asymptotic distributions of the maximum likelihood is used to obtain confidence intervals for the parameters. Monte Carlo simulations are carried out to investigate the performance of the new distribution. Finally, a real data set is analyzed to illustrate the results.

show abstract

A Marshall-Olkin Power Log-normal Distribution and Its Applications to Survival Data

Cited by 19 publications

References 20 publications

Using the Marshall-Olkin Extended Zipf Distribution in Graph Generation

Using the Marshall-Olkin Extended Zipf Distribution in Graph Generation

The Marshall–Olkin Generalized Inverse Weibull Distribution: Properties and Application

Marshall-Olkin extended Burr III distribution

Contact Info

Product

Resources

About