Transmuted Arcsine Distribution Properties and Application

Bleed, Salma Omar; Abdelali, Arwa Elsunousi Ali

doi:10.29121/granthaalayah.v6.i10.2018.1159

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…For instance, in 2013 [13], the authors introduced the transmuted Log-Logistic distribution, while in 2016 [14], others proposed the extended arcsine distribution, a continuous model characterized by two parameters, achieved through the method of exponentiated generalized. The distribution of the quadratic formula (L), accompanied by a comprehensive study of distribution characteristics was presented in [15].…”

Section: Introductionmentioning

confidence: 99%

Transforming data with the arcsine distribution for random walks

Laarichi,

Elkaf,

Aloui

et al. 2024

Math. models, eng.

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This article delves into a pioneering methodology for optimizing the analysis of random walk data by implementing the arcsine distribution. The application of the arcsine distribution serves as an imperceptible yet potent solution, mitigating asymmetry and introducing bounds while effectively modeling the nuanced characteristics intrinsic to random walk patterns. Through a meticulous exploration of the mathematical foundations and practical applications of this distribution, this study discreetly advances statistical methodologies for handling random walk data. The article illuminates the theoretical underpinnings, subtle advantages, and pragmatic implications of arcsine distribution utilization, showcasing its imperceptible yet impactful role in capturing and reshaping random walk dynamics. Through a meticulous exploration of the mathematical foundations and practical applications of this distribution, this study discreetly advances statistical methodologies for handling random walk data, particularly in the context of financial modeling.

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Section: Introductionmentioning

confidence: 99%

Transforming data with the arcsine distribution for random walks

Laarichi,

Elkaf,

Aloui

et al. 2024

Math. models, eng.

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A New Family of Lifetime Models: Theoretical Developments with Applications in Biomedical and Environmental Data

Elbatal

Khan

Hussain

et al. 2022

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With the aim of identifying a probability model that not only correctly describes the stochastic behavior of extreme environmental factors such as excess rain, acid rain pH level, and concentrations of ozone, but also measures concentrations of NO2 and leads deliberations, etc., for a specific site or multiple site forms as well as for life testing experiments, we introduced a novel class of distributions known as the Sine Burr X−G family. Some exceptional prototypes of this class are proposed. Statistical assets of the presented class, such as density function, complete and incomplete moments, average deviation, and Lorenz and Bonferroni graphs, are proposed. Parameter estimation is made via the likelihood method. Moreover, the application is explained by using four real data sets. We have also illustrated the significance and elasticity of the proposed class in the above-mentioned stochastic phenomenon.

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Modeling to Factor Productivity of the United Kingdom Food Chain: Using a New Lifetime-Generated Family of Distributions

et al. 2022

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This article proposes a new lifetime-generated family of distributions called the sine-exponentiated Weibull-H (SEW-H) family, which is derived from two well-established families of distributions of entirely different nature: the sine-G (S-G) and the exponentiated Weibull-H (EW-H) families. Three new special models of this family include the sine-exponentiated Weibull exponential (SEWEx), the sine-exponentiated Weibull Rayleigh (SEWR) and sine-exponentiated Weibull Burr X (SEWBX) distributions. The useful expansions of the probability density function (pdf) and cumulative distribution function (cdf) are derived. Statistical properties are obtained, including quantiles (QU), moments (MO), incomplete MO (IMO), and order statistics (OS) are computed. Six numerous methods of estimation are produced to estimate the parameters: maximum likelihood (ML), least-square (LS), a maximum product of spacing (MPRSP), weighted LS (WLS), Cramér–von Mises (CRVM), and Anderson–Darling (AD). The performance of the estimation approaches is investigated using Monte Carlo simulations. The total factor productivity (TFP) of the United Kingdom food chain is an indication of the efficiency and competitiveness of the food sector in the United Kingdom. TFP growth suggests that the industry is becoming more efficient. If TFP of the food chain in the United Kingdom grows more rapidly than in other nations, it suggests that the sector is becoming more competitive. TFP, also known as multi-factor productivity in economic theory, estimates the fraction of output that cannot be explained by traditionally measured inputs of labor and capital employed in production. In this paper, we use five real datasets to show the relevance and flexibility of the suggested family. The first dataset represents the United Kingdom food chain from 2000 to 2019, whereas the second dataset represents the food and drink wholesaling in the United Kingdom from 2000 to 2019 as one factor of FTP; the third dataset contains the tensile strength of single carbon fibers (in GPa); the fourth dataset is often called the breaking stress of carbon fiber dataset; the fifth dataset represents the TFP growth of agricultural production for thirty-seven African countries from 2001–2010. The new suggested distribution is very flexible and it outperforms many known distributions.

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Transmuted Arcsine Distribution Properties and Application

Cited by 3 publications

References 5 publications

Transforming data with the arcsine distribution for random walks

Transforming data with the arcsine distribution for random walks

A New Family of Lifetime Models: Theoretical Developments with Applications in Biomedical and Environmental Data

Modeling to Factor Productivity of the United Kingdom Food Chain: Using a New Lifetime-Generated Family of Distributions

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