Marshall-Olkin Extended Inverse Weibull Distribution and Its Application

pakungwati, ratu mutiara; Widyaningsih, Yekti; Lestari, Dian

doi:10.1088/1742-6596/1108/1/012114

Cited by 13 publications

(11 citation statements)

References 13 publications

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“…The data are 0.39, 0.85, 1.08,1. 25 We conclude from the values of the goodness-of-fit statistics A * , W * , K-S and the p-value of the K-S statistic that the BIII-TL-W model fits the carbon fibres data set better than the several models considered in this paper. The values of the SS also show that the BIII-TL-W model performs better than the selected models as shown in Figure 7.…”

Section: MM Carbon Fibres Data Setmentioning

confidence: 56%

“…[ 24 ] Marshall-Olkin-inverse Weibull (MO-IW) by Pakungwati et al. [ 25 ] Kumaraswamy odd Lindley-Log logistic (KOL-LLoG) by Chipepa et al. [ 26 ], Kumaraswamy-Weibull (KW) by Cordeiro et al.…”

Section: Applicationsmentioning

confidence: 99%

“…The non-nested models considered in this paper are: exponentiated Weibull (EW) by Pal et al [24] Marshall-Olkin-inverse Weibull (MO-IW) by Pakungwati et al [25] Kumaraswamy odd Lindley-Log logistic (KOL-LLoG) by Chipepa et al [26], Kumaraswamy-Weibull (KW) by Cordeiro et al [27], beta odd Lindley-exponential (BOL-E) and beta odd Lindley-uniform by Chipepa et al [28] the Topp-Leone-Weibull-Lomax (TLWLx) distributions by Jamal et al [29] and the exponential Lindley Table 1. Monte Carlo simulation results for BIII-TL-W distribution: Mean, RMSE and average bias.…”

Section: Applicationsmentioning

confidence: 99%

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The Burr III-Topp-Leone-G family of distributions with applications

Chipepa

Oluyede

Peter

2021

Heliyon

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In this article, we present a new family of generalized distributions called the Burr III-Topp-Leone-G (BIII-TL-G). We further study in detail its structural properties including moments, probability weighted moments, distribution of order statistics, and entropy. The maximum likelihood estimation method is used to estimate the model parameters. Simulations are carried out to show the consistency and efficiency of parameter estimates and finally, real data sets are used to demonstrate the applicability of the proposed model.

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Section: MM Carbon Fibres Data Setmentioning

confidence: 56%

Section: Applicationsmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

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The Burr III-Topp-Leone-G family of distributions with applications

Chipepa

Oluyede

Peter

2021

Heliyon

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“…We compared the MO-HL-W distribution with other competing three parameter non-nested models: the exponentiated-Fréchet (EFr) distribution by [Nadarajah and Kotz , 2003], other two non-nested studied by , namely, Marshall-Olkin extended Fréchet (MOEFr) and Marshall-Olkin extended generalized exponential (MOEGE) distributions, Marshall-Olkin extended inverse Weibull (IWMO) by [Pakungwati et al , 2018], exponentiated Weibull by [Pal et al , 2006] and alpha power Weibull (APW) by [Nassar et al , 2018] distributions. The pdfs of the non-nested models are given by:…”

Section: Applicationsmentioning

confidence: 99%

The Marshall-Olkin Half Logistic-G Family of Distributions With Applications

Makubate¹,

Chipepa²,

Oluyede³

et al. 2021

IJSP

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Attempts have been made to define new classes of distributions that provide more flexibility for modeling data that is skewed in nature. In this work, we propose a new family of distributions namely the Marshall-Olkin Half Logistic-G (MO-HL-G) based on the generator pioneered by [Marshall and Olkin , 1997]. This new family of distributions allows for a flexible fit to real data from several fields, such as engineering, hydrology, and survival analysis. The structural properties of these distributions are studied and its model parameters are obtained through the maximum likelihood method. We finally demonstrate the effectiveness of these models via simulation experiments.

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“…Plots of the fitted densities, the histogram of the data and probability plots (Chambers, Cleveland, Kleiner and Tukey (1983)) are also presented to show how well our model fits the observed data sets compared to the selected non-nested models. The plots are shown in Figures 4 (a We compared the MO-HL-W distribution with other competing three parameter non-nested models: the exponentiated-Fréchet (EFr) distribution by [Nadarajah and Kotz , 2003], other two non-nested studied by , namely, Marshall-Olkin extended Fréchet (MOEFr) and Marshall-Olkin extended generalized exponential (MOEGE) distributions, Marshall-Olkin extended inverse Weibull (IWMO) by [Pakungwati et al , 2018], exponentiated Weibull by [Pal et al , 2006] and alpha power Weibull (APW) by [Nassar et al , 2018] distributions. The pdfs of the non-nested models are given by:…”

Section: Applicationsmentioning

confidence: 99%