Statistical analysis for Kumaraswamy’s distribution based on record data

Nadar, Mustafa; Papadopoulos, Alexander; Kızılaslan, Fatih

doi:10.1007/s00362-012-0432-7

Cited by 49 publications

(13 citation statements)

References 36 publications

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“…based on such a record sample, Bayesian prediction is needed for the ℎ lower record, 1 < m < s. The conditional pdf of Y given the parameter is given by (22), it can be written as The Bayes predictive density function of = ( ) given the past m lower records is given by (24), it can be written in the form * ( | ) = −1…”

Section: Bayesian Prediction Of Future Recordsmentioning

confidence: 99%

Inference for exponentiated general class of distributions based on record values

Sindi

AL-Dayian

Shahbaz

2017

Pak.j.stat.oper.res.

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The main objective of this paper is to suggest and study a new exponentiated general class (EGC) of distributions. Maximum likelihood, Bayesian and empirical Bayesian estimators of the parameter of the EGC of distributions based on lower record values are obtained. Furthermore, Bayesian prediction of future records is considered. Based on lower record values, the exponentiated Weibull distribution, its special cases of distributions and exponentiated Gompertz distribution are applied to the EGC of distributions.

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Section: Bayesian Prediction Of Future Recordsmentioning

confidence: 99%

Inference for exponentiated general class of distributions based on record values

Sindi

AL-Dayian

Shahbaz

2017

Pak.j.stat.oper.res.

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“…The different estimation methods for the unknown parameters of the KU distribution have been considered by [6]. The point estimations for this distribution under complete and record samples have been studied by [13,16]. Furthermore, for more details about the other extensions of this distribution, see [7,22,25].…”

Section: Introductionmentioning

confidence: 99%

Interval estimation of Kumaraswamy parameters based on progressively type II censored sample and record values

Panahi¹

2020

MMN

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In this paper, interval estimation for the parameters of the Kumaraswamy distribution is studied based on progressively censoring scheme and the record values. Some pivotal quantities and Theorems are proposed to construct the exact confidence interval for the one-shape parameter and the joint confidence region for the two-shape parameters. Simulation study is performed to investigate the coverage probabilities of the proposed confidence under progressively censored sample and upper record data. Finally, the proposed intervals and regions have been studied based on two real data sets as the illustrative examples. The first data set contains the strength characterization of brittle material and the second data set consists of the daily average wind speeds.

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“…Nader et al . [23] used the Maximum likelihood and Bayesian approaches to estimator the parameters of k distribution. Lemonte et al [19] derived general formulas for moment generating function, mean deviation , Bonferroni and Lorentz curves , density of order statistics of three parameters of exponentiated kumaraswamy Inference on the log-eponentiated Kumaraswamy distribution 167 (EK) distribution.…”

Section: Introductionmentioning

confidence: 99%

Inference on the log-exponentiated Kumaraswamy distribution

Mohammed¹

2017

ijcms

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In this paper, the log-exponentiated Kumaraswamy (LEK) distribution is introduced and studied as a survival model of unemployment, its survived function has the interesting property that it can be decreasing depending on the shape parameters. The method of maximum likelihood is applied for estimating the model parameters, survival and hazard rate functions. Stratification is used to reduce heterogeneity in survival unemployment data. To achieve this aim, three models are considered. In the first model, unemployment experience for all working ages (15-60) is studied. In the second model, unemployment experience for ages (30-60) is studied and in the third model, unemployment experience for ages (45-60) is studied. Unemployment is modeled as a function of age. The distribution of unemployment with respect to age is represented by the LEK distribution or the three suggested models. For different values of samples sizes, Monte Carlo simulation is performed to investigate the precision of maximum likelihood estimates.

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Statistical analysis for Kumaraswamy’s distribution based on record data

Cited by 49 publications

References 36 publications

Inference for exponentiated general class of distributions based on record values

Inference for exponentiated general class of distributions based on record values

Interval estimation of Kumaraswamy parameters based on progressively type II censored sample and record values

Inference on the log-exponentiated Kumaraswamy distribution

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