Marshall Olkin Alpha Power Extended Weibull Distribution: Different Methods of Estimation based on Type I and Type II Censoring

Abstract

Highlights• A new extended Weibull distribution is offered.• Essential properties are studied.• MOAPEW based on Type I and Type II censored samples are examined.• Parameter estimation using classical methods and Bayesian were obtained.• Simulation studies Application and the application of real data are used.

Half Logistic Inverted Nadarajah–Haghighi Distribution under Ranked Set Sampling with Applications

“…The analysis of three real-world datasets, one of which is linked to COVID-19 data and another to yearly flood flows, are discussed in this part to confirm that the HL-INH distribution is the best model fitting present in the literature. The models used for comparison are OLINH; PINH; INH; EOWINH; MOINH; extended Weibull (EW), which was discussed by [52]; exponential Lomax (EL), which was discussed by [53]; Weibull-Lomax (WL), which was discussed by [54]; and Gumbel-Lomax (GL), which was discussed by [55].…”

A new Topp-Leone Kumaraswamy Marshall-Olkin generated family of distributions with applications

“…In this article, we will discuss a newly developed broad family of distributions that was produced by mixing the Topp-Leone distribution [1] , with other distributions as the Generated families of Kumaraswamy [2] , Kumaraswamy censored model [3] and Marshall-Olkin [4] . Typical instances of this family of distributions include the Marshall-Olkin Topp Leone-G [5] , [6] , type II half logistic [7] , exponentiated generalized Topp-Leone-G [8] , Garhy-G [9] , half-logistic odd Weibull-Topp-Leone-G [10] , truncated inverted Kumaraswamy-G [11] , Topp-Leone Kumaraswamy-G [12] , odd log-logistic Poisson-G [13] , Kumaraswamy-G [14] , type II power Topp-Leone-G [15] , Fréchet Topp-Leone-G [16] , Topp-Leone Gompertz-G [17] , Topp-Leone odd Lindley-G [18] , type II Topp-Leone-G [19] , Topp–Leone modified Weibull [20] , Marshall-Olkin extended Gompertz Makeham [21] , Marshall Olkin alpha power extended Weibull [22] , Marshall-Olkin alpha power inverse Weibull [23] , Marshall-Olkin alpha power Lomax [24] , [25] , a generalized Birnbaum-Saunders distribution [26] , Topp–Leone modified Weibull model [20] , a new version of Topp–Leone distribution with engineering applications [27] , reliability analysis of exponential distributions [28] , Marshall-Olkin improved Rayleigh distribution [29] , [30] . Some authors worked on Bayesian inferences such as [31] and a comprehensive study of lognormality tests [32] .…”

Self‐attention based progressive generative adversarial network optimized with arithmetic optimization algorithm for kidney stone detection

“…Then the segmented image is given to the feature extraction by using term frequency-inverse document frequency 21 to extract the features. Then the extracted features are fed to feature selection using WDGMS 22 for selecting the features. Them the selected features are given to SPGGAN 23 for detecting kidney stone.…”

“…Then the segmented image is given to the feature extraction by using term frequency‐inverse document frequency 21 to extract the features. Then the extracted features are fed to feature selection using WDGMS 22 for selecting the features. Them the selected features are given to SPGGAN 23 for detecting kidney stone. Generally, SPGGAN not express any adoption of optimization strategies for scaling the optimum parameters and assuring exact kidney stone detection. Therefore, proposed AOA 24 is considered to optimize the weight parameters of SPGGAN. The proposed approach is done in python, its performance is examined under certain performance metrics, like accuracy, precision, sensitivity, F‐measure, specificity, computational time, ROC. The performance of the proposed SPGGAN‐AOA‐KSD approach is assessed with existing methods, like ANN‐OGGA‐KSD, 25 HMANN‐BPA‐KSD, 26 and ANN‐CSOA‐KSD 27 respectively.…”