2019
DOI: 10.3390/e21111089
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Truncated Inverted Kumaraswamy Generated Family of Distributions with Applications

Abstract: In this article, we introduce a new general family of distributions derived to the truncated inverted Kumaraswamy distribution (on the unit interval), called the truncated inverted Kumaraswamy generated family. Among its qualities, it is characterized with tractable functions, has the ability to enhance the flexibility of a given distribution, and demonstrates nice statistical properties, including competitive fits for various kinds of data. A particular focus is given on a special member of the family defined… Show more

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Cited by 35 publications
(20 citation statements)
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References 17 publications
(18 reference statements)
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“…The second data set (0.1, 0.33, 0.44, 0.56, 0.59, 0.72, 0.74, 0.77, 0.92, 0.93, 0.96, 1, 1, 1.02, 1.05, 1.07, 07, 1.08, 1.08, 1.08, 1.09, 1.12, 1.13, 1.15, 1.16, 1.2, 1.21, 1.22, 1.22, 1.24, 1.3, 1.34, 1.36, 1.39, 1.44, 1.46, 1.53, 1.59, 1.6, 1.63, 1.63, 1.68, 1.71, 1.72, 1.76, 1.83, 1.95, 1.96, 1.97 (1960). Many other real data sets related to failure times can be found in [13][14][15][16][17][18][19][20]. Figure 3 gives the total time test (TTT) plots.…”
Section: Modelingmentioning
confidence: 99%
“…The second data set (0.1, 0.33, 0.44, 0.56, 0.59, 0.72, 0.74, 0.77, 0.92, 0.93, 0.96, 1, 1, 1.02, 1.05, 1.07, 07, 1.08, 1.08, 1.08, 1.09, 1.12, 1.13, 1.15, 1.16, 1.2, 1.21, 1.22, 1.22, 1.24, 1.3, 1.34, 1.36, 1.39, 1.44, 1.46, 1.53, 1.59, 1.6, 1.63, 1.63, 1.68, 1.71, 1.72, 1.76, 1.83, 1.95, 1.96, 1.97 (1960). Many other real data sets related to failure times can be found in [13][14][15][16][17][18][19][20]. Figure 3 gives the total time test (TTT) plots.…”
Section: Modelingmentioning
confidence: 99%
“…By the use of well-known general families of distributions, one can extend the truncated Cauchy distribution in multiple theoretical or applied directions. For instance, one can use the exp-G family proposed by [ 8 ], the Kumaraswamy-G family introduced by [ 9 ], the beta-G family developed by [ 10 ], the Marshall-Olkin-G family proposed by [ 11 ], the Weibull-G family developed by [ 12 , 13 ], the transmuted-G family developed by [ 14 ], the gamma-G family proposed by [ 15 ], the inverse exponential-G family proposed by [ 16 ], the sine-G family introduced by [ 17 ], and the truncated inverted Kumaraswamy-G family proposed by [ 18 ]. The idea behind this general families is to transform or add (one or several) parameters to a baseline distribution in order to improve its global flexibility, with the aim to gain on the fitting of the resulting models.…”
Section: Introductionmentioning
confidence: 99%
“…The common scenario consists in truncating the cdf a flexible distribution (inverse or not, generally with support on (0, +∞) or R) over the interval (0, 1) and compounding it by a simple baseline cdf. Current developments include the truncated Fréchet-G (TF-G) family by [17], truncated Burr-G (TB-G) family by [18], truncated Cauchy power-G (TCP-G) family by [19], truncated inverted Kumaraswamy-G (TIK-G) family by [20], and truncated inverse Weibull-G (TIW-G) family, also called type II truncated Fréchet-G (TIITF-G) family, by [21].…”
Section: Introductionmentioning
confidence: 99%