Estimation and prediction for a progressively censored generalized inverted exponential distribution

Dey, Sanku; Singh, Sukhdev; Tripathi, Yogesh Mani; Asgharzadeh, A.

doi:10.1016/j.stamet.2016.05.007

Cited by 87 publications

(41 citation statements)

References 26 publications

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“…Oguntunde and Adejumo [15] have explored the statistical properties of the generalized inverted generalized exponential distribution and its parameters were estimated at both censored and uncensored cases using the method of maximum likelihood estimation (MLE). Dey et al [16] presents some estimation and prediction of unknown parameters based on progressively censored generalized Inverted Exponential data.…”

Section: Original Research Articlementioning

confidence: 99%

A New Extended Generalized Inverse Exponential Distribution: Properties and Applications

Sule¹

2021

AJPAS

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The quest by researchers in the area of distribution theory in proposing new models with greater flexibility has filled literature. On this note, we proposed a new distribution called the new extended generalized inverse exponential distribution with five positive parameters, which extends and generalizes the extended generalized inverse exponential distribution. We derive some mathematical properties of the proposed model including explicit expressions for the quantile function, moments, generating function, survival, hazard rate, reversed hazard rate, cumulative hazard rate function and odds functions. The method of maximum likelihood is used to estimate the parameters of the distribution. We illustrate its potentiality with applications to three real life data sets which show that the new extended generalized inverse exponential model provides greater flexibility and better fit than other competing models considered.

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Section: Original Research Articlementioning

confidence: 99%

A New Extended Generalized Inverse Exponential Distribution: Properties and Applications

Sule¹

2021

AJPAS

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“…For more details, see Calabria and Pulcini [ 35 ] and Dey et al [ 36 ]. Next, we provide the posterior probability distribution for a complete data set.…”

Section: Bayesian Estimationmentioning

confidence: 99%

On Modeling the Earthquake Insurance Data via a New Member of the T-X Family

Ahmad

Mahmoudi

Kharazmi

2020

Computational Intelligence and Neuroscience

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Heavy-tailed distributions play an important role in modeling data in actuarial and financial sciences. In this article, a new method is suggested to define new distributions suitable for modeling data with a heavy right tail. The proposed method may be named as the Z-family of distributions. For illustrative purposes, a special submodel of the proposed family, called the Z-Weibull distribution, is considered in detail to model data with a heavy right tail. The method of maximum likelihood estimation is adopted to estimate the model parameters. A brief Monte Carlo simulation study for evaluating the maximum likelihood estimators is done. Furthermore, some actuarial measures such as value at risk and tail value at risk are calculated. A simulation study based on these actuarial measures is also done. An application of the Z-Weibull model to the earthquake insurance data is presented. Based on the analyses, we observed that the proposed distribution can be used quite effectively in modeling heavy-tailed data in insurance sciences and other related fields. Finally, Bayesian analysis and performance of Gibbs sampling for the earthquake data have also been carried out.

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“…Notice, that to obtain the hyperparameter values under IN prior, we first generate 1,000 number of complete samples from PE distribution with k ¼ 2 and h ¼ 0:5, each sample with 30 observations. Now corresponding to each sample, we first obtain the maximum likelihood estimates of k and h, and then compare the mean and variance of these samples with the mean and variance of the considered priors (see Dey et al (2016); Singh and Tripathi, 2018 for more details). Subsequently, we get the hyper-parameter values as a 1 ¼ 16:5443; b 1 ¼ 7:3979; a 2 ¼ 1:2145, and b 2 ¼ 1:3608.…”

Section: Simulation Studymentioning

confidence: 99%

Estimation and Prediction for the Poisson-Exponential Distribution Based on Type-II Censored Data

Belaghi

Asl

Alma

et al. 2018

American Journal of Mathematical and Management Sciences

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observed under type-II censoring. We obtain maximum likelihood estimates and associated interval estimates under a classical approach, and Bayes estimates using various loss functions and associated highest posterior density interval estimates. Maximum likelihood estimates are obtained using the Newton-Raphson method and Expectation Maximization (EM) algorithm, and Bayes estimates are computed using importance sampling and Lindley approximation. We also compute shrinkage preliminary test estimates based on maximum likelihood and Bayes estimates. Further, we provide inference on the censored observations by making use of best unbiased and condition median predictors under a classical approach, and predictive estimates under the Bayesian paradigm using importance sampling. The associated predictive interval estimates are also obtained using different methods. Finally, we conduct a simulation study to compare the performance of all the proposed methods of estimation and prediction, and analyze a real data set for illustration purpose.

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Estimation and prediction for a progressively censored generalized inverted exponential distribution

Cited by 87 publications

References 26 publications

A New Extended Generalized Inverse Exponential Distribution: Properties and Applications

A New Extended Generalized Inverse Exponential Distribution: Properties and Applications

On Modeling the Earthquake Insurance Data via a New Member of the T-X Family

Estimation and Prediction for the Poisson-Exponential Distribution Based on Type-II Censored Data

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