A New Distribution for Modeling Data with Increasing Hazard Rate: A Case of COVID-19 Pandemic and Vinyl Chloride Data

Tolba, Ahlam H.; Onyekwere, Chrisogonus K.; El-Saeed, Ahmed R.; Alsadat, Najwan; Alohali, Hanan; Obulezi, Okechukwu J.

doi:10.3390/su151712782

Cited by 5 publications

(1 citation statement)

References 34 publications

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“…Observed Expected q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q 0.0 0.4 0.8 0.0 0.2 0.4 0.6 0.8 1.0 Rama Observed Expected q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q 0.0 0.4 0.8 The second data is on Vinyl chloride data from clean upgradient ground-water monitoring wells in (g/L) studied by [13] in Table 4. Again, the measures for fitness used are the negative Log-Likelihood (NLL), Akaike Information Criterion (AIC), Corrected AIC (CAIC), Bayesian Information Criterion (BIC), HannanâQuinn information criterion (HQIC), Cramer von mises (W * ), Anderson Darling (A * ), Kolmogorov-Smirnov (K-S) statistic and the p-value.…”

Section: Chris−jerrymentioning

confidence: 99%

A New Modification of Shanker Distribution with Applications to Increasing Failure Rate Data

Etaga,

Celestine,

Onyekwere

et al. 2023

EJMS

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In this paper, a new distribution is proposed by mixing the exponential distribution and the Shanker distribution with a mixing proportion being the same as those that yielded the Shanker distribution. The proposed distribution is referred to as the XShanker distribution. The distributional properties of the XShanker distribution namely, quantile function, moments and their associated measures,the mode, moment generating function, characteristic function, distribution of order statistics, and entropy are derived and studied. The reliability analysis shows that the failure rate is a strictly increasing function. The parameter of the model was estimated using the maximum likelihood function. We illustrated the usefulness of XShanker distribution using data on waiting times of 100 bank customers and vinyl chloride from clean ungradient ground-water monitoring wells in (g/L).

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Section: Chris−jerrymentioning

confidence: 99%

A New Modification of Shanker Distribution with Applications to Increasing Failure Rate Data

Etaga,

Celestine,

Onyekwere

et al. 2023

EJMS

Self Cite

View full text Add to dashboard Cite

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A new reduced quantile function for generating families of distributions

Prince O,

Etaga O,

Precious

et al. 2024

Ann Math Phys

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In this paper, a variant of the T-X(Y) generator was developed by suppressing the scale parameter of the classical Lomax distribution in the quantile function. Uniquely, the reduction of the number of parameters essentially accounts for the parsimony of the attendant model. The study considered the Exponential distribution as the transformer and consequently obtained the New Reduced Quantile Exponential-G (NRQE-G) family. The Type-II Gumbel distribution was deployed as the baseline to obtain a special sub-model known as the New Reduced Quantile Exponential Type-II Gumbel (NRQE-T2G) model. Some functional properties of the distribution namely, moment and its related measures such as the mean, variance, second, third, and fourth moments were obtained. The Mode, skewness, Kurtosis, index of dispersion, coefficient of variation, order statistics, survival, hazard, and quantile function were also derived. The maximum likelihood estimation method was used to estimate its parameters. The model's credibility, applicability, and flexibility were proven using two real-life datasets.

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Statistical Inferences about Parameters of the Pseudo Lindley Distribution with Acceptance Sampling Plans

Eissa,

Sonar,

Alamri

et al. 2024

Axioms

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Different non-Bayesian and Bayesian techniques were used to estimate the pseudo-Lindley (PsL) distribution’s parameters in this study. To derive Bayesian estimators, one must assume appropriate priors on the parameters and use loss functions such as squared error (SE), general entropy (GE), and linear-exponential (LINEX). Since no closed-form solutions are accessible for Bayes estimates under these loss functions, the Markov Chain Monte Carlo (MCMC) approach was used. Simulation studies were conducted to evaluate the estimators’ performance under the given loss functions. Furthermore, we exhibited the adaptability and practicality of the PsL distribution through real-world data applications, which is essential for evaluating the various estimation techniques. Also, the acceptance sampling plans were developed in this work for items whose lifespans approximate the PsL distribution.

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A New Distribution for Modeling Data with Increasing Hazard Rate: A Case of COVID-19 Pandemic and Vinyl Chloride Data

Cited by 5 publications

References 34 publications

A New Modification of Shanker Distribution with Applications to Increasing Failure Rate Data

A New Modification of Shanker Distribution with Applications to Increasing Failure Rate Data

A new reduced quantile function for generating families of distributions

Statistical Inferences about Parameters of the Pseudo Lindley Distribution with Acceptance Sampling Plans

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