A new statistical distribution: cubic rank transmuted Kumaraswamy distribution and its properties

Saraçoğlu, Buğra; Tanış, Caner

doi:10.4038/jnsfsr.v46i4.8626

Cited by 11 publications

(8 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the real data analysis, the dataset from Dasgupta R [21] was considered first and from Table 3 shows that the CRTIW distribution has the best fit because it possesses the smallest AIC, BIC, AICc and -2loglikelihood among all fitted models and the same is also applicable for the data set of Cordeiro and Don Santos Brito [1], Brito RS. [22] and Bugra et al [18], has presented in Table 6. Based on our findings, CRTIW distribution can be used to model the two data sets.…”

Section: Resultsmentioning

confidence: 99%

“…Granzotto et al [9] have suggested cubic rank transmuted Weibull, cubic rank transmuted loglogistic distributions etc. Bugra Saracoglu et al [18], studied the properties of cubic rank transmuted kumaraswamy distribution, Cubic transmuted Gompertz distribution was studied by Ogunde AA et al [19] etc.…”

Section: Cubic Rank Transmuted Kumaraswamy (Crtiw) Distributionmentioning

confidence: 99%

“…The second dataset is about the total milk production in the first birth of 107 cows living in Carnauba farm in Brazil. This data exist in the work of Cordeiro and Brito [1] and Brito [22] and it has also been used by Yousof et al [23], Bugra S et al [18]. The data set is given has follows: 0.…”

Section: Application To Real Life Datamentioning

confidence: 99%

See 2 more Smart Citations

The Characterization of the Cubic Rank Inverse Weibull Distribution

Ogunde

Chukwu

Agwuegbo

2020

ARJOM

View full text Add to dashboard Cite

This work provides a new statistical distribution named Cubic rank transmuted Inverse Weibull distribution which was developed using the cubic transmutation map. Various statistical properties of the new distribution which includes: hazard function, moments, moment generating function, skewness, kurtosis, Renyl entropy and the order statistics were studied. A maximum likelihood estimation method was used in estimating the parameters of the distribution. Applications to real data set show the tractability of the distribution over other distributions and its sub-model.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Cubic Rank Transmuted Kumaraswamy (Crtiw) Distributionmentioning

confidence: 99%

See 1 more Smart Citation

The Characterization of the Cubic Rank Inverse Weibull Distribution

Ogunde

Chukwu

Agwuegbo

2020

ARJOM

View full text Add to dashboard Cite

show abstract

“…The lists of several cubic transmuted distributions introduced by various researchers are mentioned in Table 2. Granzotto et al, [2] AL-Kadim and Mohammed [97] Rahman et al, [5] Rahman et al, [99] Ansari and Eledum [100] Rahman et al, [95] Celik [101] Celik [101] Celik [101] Saraҫoğlu and Tanɩş [102] Riffi and Hamdan [103] Ansari et al, [104] Rahman et al, [105] Bhatti et al, [106] Rahman et al, [96] Adeyinka [107] Cubic Transmuted Weibull…”

Section: Cubic Rank Transmutation Mapmentioning

confidence: 99%

Generalized Rank Mapped Transmuted Distributions with Properties and Application: A Review

Imliyangba

Das

Chettri

2021

AJPAS

View full text Add to dashboard Cite

Generalizing probability distributions is a very common practice in the theory of statistics. Researchers have proposed several generalized classes of distributions which are very flexible and convenient to study various statistical properties of the distribution and its ability to fit the real-life data. Several methods are available in the literature to generalize new family of distributions. The Quadratic Rank Transmutation Map (QRTM) is a tool for the construction of new families of non-Gaussian distributions and to modulate a given base distribution for modifying the moments like the skewness and kurtosis with the ability to explore its tail properties and improve the adequacy of the distribution. Recently, a new family of transmutation map, defined as Cubic Rank Transmutation (CRT) has been used by several authors to develop new distributions with application to real-life data. In this article, we have done a review work on the existing generalized rank mapped transmuted probability distributions, available in the literature with various statistical properties such as the reliability, hazard rate and cumulative hazard functions, moments, mean, variance, moment-generating function, order statistics, generalized entropy and quantile function along with its applications. Some future works have also been discussed for generalized rank mapped transmuted distributions.

show abstract

“…Other commonly used distributions to modeled big survival data with extreme observations are Weibull, gamma, Gompertz, log-normal, loglogistic, generalized gamma, and generalized F-distribution. Also, the FPM can efficiently incorporate covariates to investigate the dependence of survival response in the context of estimated parameters, survival function, and cumulative hazard function [6][7][8][9][10]. Multivariate regression models assume that there is no multicollinearity among covariates of survival systems.…”

Section: Introductionmentioning

confidence: 99%

A Flexible and Robust Approach to Analyze Survival Systems in the Presence of Extreme Observations

Sadiq

Mehmood

2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Survival systems are difficult to analyze in the presence of extreme observations and multicollinearity. Finding appropriate models that provide a robust description of such survival systems and that address the smooth hazards in the context of covariates can be challenging given the sheer number of possibilities. Survival time algorithms that evaluate the efficiency of models in the presence of extreme observations over different datasets provide an effective tool to identify robust systems. However, the existing algorithms addressing the analysis of survival systems are limited in long-term evaluations. Therefore, an algorithm that can analyze survival time response on high-dimensional complex survival systems having extreme observations is developed which explores large margins dynamically. This algorithm is developed as a conjugate of flexible parametric models and partial least squares to estimate smooth, flexible, and robust functions to extrapolate the survival model in long-term evaluations in the presence of extreme observations. The algorithm is tested and validated using four distributions based on a simulated dataset generated from the Weibull distribution and compared with partial least squares-Cox regression. The comparison shows its flexibility and efficiency in handling different survival systems in the presence of extreme values. The algorithm is also used to analyze four real datasets of breast cancer survival time, each containing seven gene signatures. The coefficients of significant genes for each dataset are estimated. The flexibility in handling various distributions as parametric survival models supports the application of the algorithm to a large variety of different survival problems and represents a robust statistical framework for survival analysis in the presence of extreme observations.

show abstract

A new statistical distribution: cubic rank transmuted Kumaraswamy distribution and its properties

Abstract: This article suggests a new statistical distribution using cubic rank transmutation map. Various statistical estimators of unknown parameters of this distribution are derived. A Monte Carlo simulation study based on bias and mean square error criteria of this estimator is conducted.

Cited by 11 publications

References 2 publications

The Characterization of the Cubic Rank Inverse Weibull Distribution

The Characterization of the Cubic Rank Inverse Weibull Distribution

Generalized Rank Mapped Transmuted Distributions with Properties and Application: A Review

A Flexible and Robust Approach to Analyze Survival Systems in the Presence of Extreme Observations

Contact Info

Product

Resources

About