Clustering right-skewed data stream via Birnbaum–Saunders mixture models: A flexible approach based on fuzzy clustering algorithm

Hashemi, Farzane; Naderi, Mehrdad; Mashinchi, M.

doi:10.1016/j.asoc.2019.105539

Cited by 11 publications

(2 citation statements)

References 35 publications

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“…Cordeiro et al [10] introduced the gamma BS distribution. Hashemi et al [15] proposed the normal mean-variance Lindley BS distribution. Naderi et al [24] proposed the normal mean-variance generalized BS distribution.…”

Section: Introductionmentioning

confidence: 99%

The Weibull Birnbaum-Saunders Distribution And Its Applications

Benkhelifa¹

2020

Stat., optim. inf. comput.

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A new lifetime model, with four positive parameters, called the Weibull Birnbaum-Saunders distribution is proposed. The proposed model extends the Birnbaum-Saunders distribution and provides great flexibility in modeling data in practice. Some mathematical properties of the new distribution are obtained including expansions for the cumulative and density functions, moments, generating function, mean deviations, order statistics and reliability. Estimation of the model parameters is carried out by the maximum likelihood estimation method. A simulation study is presented to show the performance of the maximum likelihood estimates of the model parameters. The flexibility of the new model is examined by applying it to two real data sets.

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Section: Introductionmentioning

confidence: 99%

The Weibull Birnbaum-Saunders Distribution And Its Applications

Benkhelifa¹

2020

Stat., optim. inf. comput.

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“…In recent decades, one of the most valuable lifetime models in applied statistics has been the two-parameter Birnbaum-Saunders (BS) distribution [19]. Properties and straightforward relationship with the normal distribution make the BS model a promising tool in engineering [20], medical sciences [21], wind analysis [22], as well as business and industry [23][24][25][26]. The BS distribution could be criticized not only for its lack of robustness against atypical observations (highly skewed and heavy-tailed data) but also for the fact that it cannot accommodate monotone (increasing or decreasing) nor bathtub-shaped hazard rate functions [27].…”

Section: Introductionmentioning

confidence: 99%