A Novel Two-parameter Nadarajah-Haghighi Extension: Properties, Copulas, Modeling Real Data and Different Estimation Methods

Shehata, Wahid A. M.; Yousof, Haitham M.

doi:10.19139/soic-2310-5070-1250

Cited by 20 publications

(4 citation statements)

References 22 publications

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“…• The CDF in (2) can be used for presenting a new discrete probability distribution for modeling the count data (see Aboraya et al [1], Ibrahim et al [30], Chesneau et al [17] and Yousof et al [60] for more details). • Many future works may be allocated to study these new bivariate Poisson Topp-Leone Burr XII distributions under some copulas (see Shehata and Yousof [50], Shehata and Yousof [51], Al-babtain et al [6], Shehata et al [53], Elgohari and Yousof [20], Elgohari and Yousof [21], Elgohari and Yousof [22], Elgohari et al [23] and Shehata et al [52]). • Some new acceptance sampling plans based on the Poisson Topp-Leone Burr XII distribution can be presented in separate article (see Ahmed and Yousof [4] and Ahmed et al [5]).…”

Section: Discussionmentioning

confidence: 99%

Modified Bagdonavicius-Nikulin Goodness-of-fit Test Statistic for the Compound Topp Leone Burr XII Model with Various Censored Applications

Khalil,

Aidi,

Ali

et al. 2024

Stat., optim. inf. comput.

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The Poisson Topp Leone Burr XII distribution is extensively studied due to its broad relevance in analyzing censored real datasets from engineering, economics, and medicine. In this research, the distribution's versatility is highlighted through the analysis of four specific real datasets. The study compares the Poisson Topp Leone Burr XII distribution with nine extensions of the Burr type XII distribution to determine which offers the best fit for these datasets. To evaluate the goodness-of-fit of the Poisson Topp Leone Burr XII distribution under right censoring, a modified Bagdonavi\v{c}ius-Nikulin goodness-of-fit test statistic is introduced and applied. This new test statistic is utilized to validate the distributional fit for the Poisson Topp Leone Burr XII distribution across the four right-censored datasets. The modified Bagdonavi\v{c}ius-Nikulin test statistic is employed to assess distributional validation, specifically in the context of right censoring. The application of this statistic involves analyzing each of the four censored datasets to confirm the appropriateness of the Poisson Topp Leone Burr XII distribution for these scenarios. Additionally, to support the evaluation of the modified goodness-of-fit test statistic, the Barzilai-Borwein algorithm is utilized. This algorithm is employed within a simulation study to further assess the effectiveness and reliability of the modified Bagdonavi\v{c}ius-Nikulin test statistic, thereby ensuring robust validation of the Poisson Topp Leone Burr XII distribution against the observed real datasets.

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Section: Discussionmentioning

confidence: 99%

Modified Bagdonavicius-Nikulin Goodness-of-fit Test Statistic for the Compound Topp Leone Burr XII Model with Various Censored Applications

Khalil,

Aidi,

Ali

et al. 2024

Stat., optim. inf. comput.

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“…Future works could be allocated for studying many new related extensions from other related aspects. e current study can be extended using neutrosophic statistics as future research (see [15][16][17][18][19][20][21]).…”

Section: Discussionmentioning

confidence: 99%

A New Probability Heavy‐Tail Model for Stochastic Modeling under Engineering Data

El-Morshedy

Eliwa

Al‐Bossly

et al. 2022

Journal of Mathematics

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The main aim of the paper is to propose and study a new heavy-tail model for stochastic modeling under engineering data. After studying and analyzing its mathematical properties, different classical estimation methods such as the ordinary least square, Cramér-von Mises, weighted least square, maximum likelihood, and Anderson–Darling estimation along with its corresponding left-tail and right-tail estimation methods are considered. Comprehensive numerical simulation studies are performed for comparing estimation methods in terms of some criterions. Three engineering and medical real-life data sets are considered for measuring the applicability flexibility of the new model and to compare the competitive models under uncensored scheme. Two engineering real-life data of them are also used to compare the classical methods. A modified Nikulin-Bagdonavicius goodness-of-fit is presented and applied accordingly for validation under censorship case. Finally, right censored lymphoma data set is analyzed under the modified statistic test for checking the validation of the reciprocal Weibull model in modeling the right censored data.

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“…In this Section, we present some new bivariate CIR (B-CIR) type models using the Farlie Gumbel Morgenstern (FGM) copula (Johnson and Kotz [36]), the modified version of the FGM copula, the Renyi's copula, the Clayton copula and also with the Ali-Mikhail-Haq copula. However, many future works could allocated to the study these new models (see also Al-babtain et al [4], Elgohari et al [20], Elgohari et al [21], Mansour et al ( [42], [43], [44], [45], [46] and [47]), Salah et al [62], Shehata and Yousof ([63], [64], [65], [66]), Ibrahim et al [34], Ali et al [5] and Ali et al [6] as examples).…”

Section: Fgm Copulamentioning

confidence: 99%

A Novel Two-Parameter Compound G Family of Probability Distributions With Some Copulas, Statistical Properties and Applications

Refaie¹,

Mahran²

2022

Stat., optim. inf. comput.

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In this work, we introduce a new G family with two-parameter called the compound reversed Rayleigh-G family. Several relevant mathematical and statistical properties are derived and analyzed. The new density can be heavy tail and right skewed with one peak, symmetric density, simple right skewed density with one peak, asymmetric right skewed with one peak and a heavy tail and right skewed with no peak. The new hazard function can be "upsidedown-constant", "constant", "increasing-constant", "revised J shape", "upside-down", "J shape" and "increasing". Many bivariate types have been also derived via di¤erent common copulas. The estimation of the model parameters is performed by maximum likelihood method. The usefulness and ‡exibility of the new family is illustrated by means of two real data sets.

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A Novel Two-parameter Nadarajah-Haghighi Extension: Properties, Copulas, Modeling Real Data and Different Estimation Methods

Cited by 20 publications

References 22 publications

Modified Bagdonavicius-Nikulin Goodness-of-fit Test Statistic for the Compound Topp Leone Burr XII Model with Various Censored Applications

Modified Bagdonavicius-Nikulin Goodness-of-fit Test Statistic for the Compound Topp Leone Burr XII Model with Various Censored Applications

A New Probability Heavy‐Tail Model for Stochastic Modeling under Engineering Data

A Novel Two-Parameter Compound G Family of Probability Distributions With Some Copulas, Statistical Properties and Applications

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