Gumbel–Logistic Unit Distribution with Application in Telecommunications Data Modeling DOI Open Access
Vladica Stojanović,

Mihailo Jovanović,

Brankica Pažun

et al.

Symmetry, Journal Year: 2024, Volume and Issue: 16(11), P. 1513 - 1513

Published: Nov. 11, 2024

The manuscript deals with a new unit distribution that depends on two positive parameters. itself was obtained from the Gumbel distribution, i.e., by its transformation, using generalized logistic mapping, into interval. In this way, so-called Gumbel-logistic (abbr. GLU) is obtained, and key properties, such as cumulative function, modality, hazard quantile moment-based characteristics, Bayesian inferences entropy, have been investigated in detail. Among others, it shown GLU unlike one which always positively asymmetric, can take both asymmetric forms. An estimation of parameters based quantiles, also performed, together asymptotic properties estimates thus their numerical simulation. Finally, has applied modeling empirical distributions some real-world data related to telecommunications.

Language: Английский

Cauchy–Logistic Unit Distribution: Properties and Application in Modeling Data Extremes DOI Creative Commons
Vladica Stojanović, Tanja Jovanović,

Radica Bojičić

et al.

Mathematics, Journal Year: 2025, Volume and Issue: 13(2), P. 255 - 255

Published: Jan. 14, 2025

This manuscript deals with a novel two-parameter stochastic distribution, obtained by transforming the Cauchy using generalized logistic mapping, into unit interval. In this way, according to well-known properties of random variable significantly accentuated values at ends interval is obtained. Therefore, proposed named Cauchy–logistic represents model that may be suitable for modeling phenomena and processes emphasized extreme values. Key CLU distribution are examined, such as moments, entropy, modality, symmetry conditions. addition, quantile-based parameter estimation procedure, an asymptotic analysis thus estimators, their Monte Carlo simulation study conducted. Finally, application in some real-world data provided.

Language: Английский

Citations

1
Laplace-Logistic Unit Distribution with Application in Dynamic and Regression Analysis DOI Open Access
Vladica Stojanović,

Tanja Jovanović Spasojević,

Mihailo Jovanović

et al.

Published: June 5, 2024

This manuscript presents a new two-parameter unit stochastic distribution, obtained by transforming the Laplace using generalized logistic map, into interval. The distribution thus is named Laplace-logistic (abbreviated LLU) and its basic properties are examined in detail. Also, procedure for estimating parameters based on quantiles provided, along with asymptotic of estimates appropriate numerical simulation study. Finally, application LLU dynamic regression analysis real-world data accentuated "peaks" "fat" tails also discussed.

Language: Английский

Citations

4
A new statistical distribution: Its empirical exploration using the reliability and lifespan data in fashion industry DOI Creative Commons
Rui Su, Najla Aloraini,

Alia A. Alkhathami

et al.

Alexandria Engineering Journal, Journal Year: 2025, Volume and Issue: 116, P. 660 - 671

Published: Jan. 9, 2025

Language: Английский

Citations

0
A Flexible Unit Distribution Based on a Half-Logistic Map with Applications in Stochastic Data Modeling DOI Open Access
Vladica Stojanović, Hassan S. Bakouch, Gadir Alomair

et al.

Symmetry, Journal Year: 2025, Volume and Issue: 17(2), P. 278 - 278

Published: Feb. 11, 2025

In this manuscript, a new two-parameter stochastic distribution is proposed and obtained by continuous half-logistic transformation of the quasi-Lindley (QL) to unit interval. The resulting distribution, named (QHU) examined in terms its key properties, such as asymmetry conditions, shape modality, moments, etc. addition, dominance with respect parameters considered, it shown that QHU contrast QL always positively asymmetric, can have both asymmetric forms. are estimated maximum likelihood (ML) method, asymptotic properties thusly estimators examined. Finally, an application modeling some real-world phenomena also presented.

Language: Английский

Citations

0
Two parameter log-Lindley distribution with LTPL web-tool DOI Creative Commons
Emrah Altun, Christophe Chesneau, Hana N. Alqifari

et al.

AIMS Mathematics, Journal Year: 2025, Volume and Issue: 10(4), P. 8306 - 8321

Published: Jan. 1, 2025

Language: Английский

Citations

0
Gumbel–Logistic Unit Distribution with Application in Telecommunications Data Modeling DOI Open Access
Vladica Stojanović,

Mihailo Jovanović,

Brankica Pažun

et al.

Published: Aug. 30, 2024

The manuscript deals with a new two-parameter unit stochastic distribution, obtained by transforming the Gumbel using generalized logistic mapping, into interval. distribution thus is called Gumbel–Logistic Unit (abbr. GLU) and its key properties have been investigated in detail. Among others, it shown that GLU unlike one which always positively asymmetric, can take both asymmetric forms. Also, procedure for estimating parameters based on quantiles, along asymptotic of estimators study their numerical simulation, presented. Finally, application modeling some real–world data related to telecommunications considered.

Language: Английский

Citations

2
Laplace-Logistic Unit Distribution with Application in Dynamic and Regression Analysis DOI Creative Commons
Vladica Stojanović, Tanja Jovanović,

Mihailo Jovanović

et al.

Mathematics, Journal Year: 2024, Volume and Issue: 12(14), P. 2282 - 2282

Published: July 22, 2024

This manuscript presents a new two-parameter unit stochastic distribution, obtained by transforming the Laplace using generalized logistic map, into interval. The distribution thus is named Laplace-logistic (abbreviated LLU) and its basic properties are examined in detail. Also, procedure for estimating parameters based on quantiles provided, along with asymptotic of estimates appropriate numerical simulation study. Finally, application LLU dynamic regression analysis real-world data accentuated “peaks” “fat” tails also discussed.

Language: Английский

Citations

1
A Bounded Lifetime Distribution Specified by a Trigonometric Function: Properties, Regression Model, and Applications DOI Creative Commons
Simon A. Ogumeyo, Festus C. Opone, Abdul Ghaniyyu Abubakari

et al.

International Journal of Mathematics and Mathematical Sciences, Journal Year: 2024, Volume and Issue: 2024(1)

Published: Jan. 1, 2024

Trigonometric functions have gained considerable attention in recent studies for developing new lifetime distributions. This is due to their parsimonious framework, mathematical tractability, and flexibility of the newly developed In this paper, Sine‐generated family used create a bounded distribution, known as Sine‐Marshall–Olkin Topp–Leone modeling data defined on unit interval. Some statistical properties including quantile function, ordinary moments, incomplete moment‐generating functions, inequality measures, Rényi entropy, probability weighted moments are derived. Seven methods parameter estimation, maximum likelihood, least squares, moment product spacing, Anderson–Darling, Cramér–von Mises estimators estimate parameters distribution. The behavior obtained from estimation investigated using Monte Carlo simulation studies. results show that asymptotically efficient consistent. distribution examined via fitting two proportional datasets. fittings performs significantly better than competing Finally, regression model presented an alternative beta Kumaraswamy models.

Language: Английский

Citations

1
New Bounded Unit Weibull Model: Applications with Quantile Regression DOI Creative Commons
Laxmi Prasad Sapkota, Nirajan Bam, Vijay Kumar

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: June 21, 2024

Abstract In practical scenarios, data measurements like ratios and proportions often fall within the 0 to 1 range. Analyzing such bounded introduces unique modeling challenges, prompting statisticians explore new distributions that can effectively handle this context. Although beta Kumaraswamy distributions, along with their related regression models, have gained popularity for examining relationship between response variables covariates, several alternative models shown superior performance compared these two. However, there is still no agreement on most effective models. Consequently, paper a novel probability distribution derived from transforming Weibull distribution. Our investigation has revealed interesting properties, including various moments generating function, entropies, quantile linear form of proposed model. Additionally, we developed sequential ratio test (SPRT) The maximum likelihood estimation method was employed estimate model parameters. A Monte Carlo simulation conducted evaluate parameter Finally, formulated applied it sets risk assessment educational attainment, demonstrating its over These results highlight importance our contributions enhancing statistical toolkit analyzing across different scientific fields.

Language: Английский

Citations

0
Gumbel–Logistic Unit Distribution with Application in Telecommunications Data Modeling DOI Open Access
Vladica Stojanović,

Mihailo Jovanović,

Brankica Pažun

et al.

Symmetry, Journal Year: 2024, Volume and Issue: 16(11), P. 1513 - 1513

Published: Nov. 11, 2024

The manuscript deals with a new unit distribution that depends on two positive parameters. itself was obtained from the Gumbel distribution, i.e., by its transformation, using generalized logistic mapping, into interval. In this way, so-called Gumbel-logistic (abbr. GLU) is obtained, and key properties, such as cumulative function, modality, hazard quantile moment-based characteristics, Bayesian inferences entropy, have been investigated in detail. Among others, it shown GLU unlike one which always positively asymmetric, can take both asymmetric forms. An estimation of parameters based quantiles, also performed, together asymptotic properties estimates thus their numerical simulation. Finally, has applied modeling empirical distributions some real-world data related to telecommunications.

Language: Английский

Citations

0