The unit-inverse Gaussian distribution: A new alternative to two-parameter distributions on the unit interval
By Mohamed E. Ghitany, Josmar Mazucheli, André Felipe Berdusco Menezes, F. Alqallaf. Communications in Statistics - Theory and Methods, 2018
120 citations recorded by the source corpus.
Abstract A new two-parameter distribution over the unit interval, called the Unit-Inverse Gaussian distribution, is introduced and studied in detail. The proposed distribution shares many properties with other known distributions on the unit interval, such as Beta, Johnson SB, Unit-Gamma, and Kumaraswamy distributions. Estimation of the parameters of the proposed distribution are obtained by transforming the data to the inverse Gaussian distribution. Unlike most distributions on the unit interval, the maximum likelihood or method of moments estimators of the parameters of the proposed distribution are expressed in simple closed forms which do not need iterative methods to compute. Application of the proposed distribution to a real data set shows better fit than many known two-parameter distributions on the unit interval.
Subfields: Civil and Structural Engineering, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/27373e087ed0f08d45bab887ef2cedc07877db6e
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Citations in the corpus
- Some New Facts about the Unit-Rayleigh Distribution with Applications (2020, methodology): “Among the most useful unit-distributions with various number of parameters, there are the power distribution, beta distribution, Johnson distribution by [5], Topp–Leone distribution by [6], Kumaraswamy distribution by [7], unit-gamma distribution by [8,9], unit-logistic distribution by [10], simplex”
- Reliability estimation in a multicomponent stress-strength model for unit Burr III distribution under progressive censoring (2020, background): “Among others, one may also refer to Hanagal (1999), Rao (2012), and Rao et al. (2015) for some more interesting results on multicomponent reliability under complete data cases. In this paper, we consider estimation of reliability in a multicomponent stress-strength model possessing k independent and”