2021
DOI: 10.15672/hujms.779454
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A new extended log-Weibull regression: Simulations and applications

Abstract: The induction of one or more parameter(s) in parent distributions opened new doors for flexible modeling in modern distribution theory. Among well-established generalized (G) classes for flexible modeling, the exponentiated-G, Marshall-Olkin-G and odd log-logistic-G families offer induction of one additional parameter while the beta-G and Kumaraswamy-G classes offer two extra shape parameters. The Marshall-Olkin-odd-loglogistic-G (MOOLL-G) family serves as an alternative to the beta-G and Kumaraswamy-G classes… Show more

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Cited by 2 publications
(1 citation statement)
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“…Let X be a random variable that describes the process under study (e.g., maximum precipitation, wind speed, temperature). If F is the cumulative distribution function (CDF) of X and x p is the associated quantile (1 − p)%, that is, F (x p ) = P (X ≤ x p ) = 1 − P (X > x p ) = 1 − p, p ∈ (0, 1), (1) then, the return period T corresponding to the return level x p is given by…”
Section: Introductionmentioning
confidence: 99%
“…Let X be a random variable that describes the process under study (e.g., maximum precipitation, wind speed, temperature). If F is the cumulative distribution function (CDF) of X and x p is the associated quantile (1 − p)%, that is, F (x p ) = P (X ≤ x p ) = 1 − P (X > x p ) = 1 − p, p ∈ (0, 1), (1) then, the return period T corresponding to the return level x p is given by…”
Section: Introductionmentioning
confidence: 99%