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Iwueze’s Distribution and Its Application

Iwueze’s Distribution and Its Application
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摘要 Increased usage of single parameter life-time distributions for reference in development of other life-time distributions and data modeling has attracted the interest of researchers. Because performance ratings differ from one distribution to another and there are increased need for distributions that delivers improved fits, new distributions with a better performance rating capable of providing improved fits have evolved in the Literature. One of such distribution is the Iwueze’s distribution. Iwueze’s distribution is proposed as a new distribution with Gamma and Exponential baseline distributions. Iwueze’s distribution theoretical density, distribution functions and statistical features such as its moments, factors of variation, skewness, kurtorsis, reliability functions, stochastic ordering, absolute deviations from average, absolute deviations from mid-point, Bonferroni and Lorenz curves, Bonferroni and Gini indexes, entropy and the stress and strength reliability have been developed. Iwueze’s distribution curve is not bell-shaped, but rather skewed positively and leptokurtic. The risk measurement function is a monotone non-decreasing function, while the average residual measurement life-time function is a monotone non-increasing function. The parameter of the Iwueze’s distribution was estimated using the likelihood estimation approach. When used for a real-life data modeling, the new proposed Iwueze’s distribution delivers improved and superior fits better than the Akshya, Shambhu, Devya, Amarendra, Aradhana, Sujatha, Akash, Rama, Shanker, Suja, Lindley, Ishita, Prakaamy, Pranav, Exponential, Ram Awadh and Odoma distributions. Iwueze’s distribution is definitely tractable and offers a better distribution than a number of well-known distributions for modeling life-time data, with greater superiority of fit performance ratings. Increased usage of single parameter life-time distributions for reference in development of other life-time distributions and data modeling has attracted the interest of researchers. Because performance ratings differ from one distribution to another and there are increased need for distributions that delivers improved fits, new distributions with a better performance rating capable of providing improved fits have evolved in the Literature. One of such distribution is the Iwueze’s distribution. Iwueze’s distribution is proposed as a new distribution with Gamma and Exponential baseline distributions. Iwueze’s distribution theoretical density, distribution functions and statistical features such as its moments, factors of variation, skewness, kurtorsis, reliability functions, stochastic ordering, absolute deviations from average, absolute deviations from mid-point, Bonferroni and Lorenz curves, Bonferroni and Gini indexes, entropy and the stress and strength reliability have been developed. Iwueze’s distribution curve is not bell-shaped, but rather skewed positively and leptokurtic. The risk measurement function is a monotone non-decreasing function, while the average residual measurement life-time function is a monotone non-increasing function. The parameter of the Iwueze’s distribution was estimated using the likelihood estimation approach. When used for a real-life data modeling, the new proposed Iwueze’s distribution delivers improved and superior fits better than the Akshya, Shambhu, Devya, Amarendra, Aradhana, Sujatha, Akash, Rama, Shanker, Suja, Lindley, Ishita, Prakaamy, Pranav, Exponential, Ram Awadh and Odoma distributions. Iwueze’s distribution is definitely tractable and offers a better distribution than a number of well-known distributions for modeling life-time data, with greater superiority of fit performance ratings.
作者 Onyemachi Elechi Emmanuel W. Okereke Iwu Hycinth Chukwudi Kiwu Lawrence Chizoba Owolabi Taiwo Wale Onyemachi Elechi;Emmanuel W. Okereke;Iwu Hycinth Chukwudi;Kiwu Lawrence Chizoba;Owolabi Taiwo Wale(Department of Statistics, Federal University of Technology, Owerri, Nigeria;Department of Statistics, Michael Okpara University of Agriculture, Umudike, Nigeria)
出处 《Journal of Applied Mathematics and Physics》 2022年第12期3783-3803,共21页 应用数学与应用物理(英文)
关键词 Life-Time Distributions Baseline Distributions Akash Distribution Sujatha Distribution Method of Maximum Likelihood Estimation Life-Time Distributions Baseline Distributions Akash Distribution Sujatha Distribution Method of Maximum Likelihood Estimation
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