The unit-inverse Gaussian distribution: A new alternative to two-parameter distributions on the unit interval

Ghitany, M. E.; Mazucheli, Josmar; Menezes, André Felipe Berdusco; Alqallaf, Fatemah

doi:10.1080/03610926.2018.1476717

Cited by 78 publications

(39 citation statements)

References 29 publications

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“…Although it has very flexible forms for data modeling, sometimes it is not sufficient for modeling and explaining unit datasets. For this reason, new alternative unit models have been proposed in the statistical distribution literature, including the Johnson S B [1], Topp-Leone [2], Kumaraswamy [3], standart two-sided power [4], log-Lindley [5], log-xgamma [6], unit Birnbaum-Saunders [7], unit Weibull [8], unit Lindley [9], unit inverse Gaussian [10], unit Gompertz [11], second degree unit Lindley [12], log-weighted exponential [13], logit slash [14], unit generalized half normal [15], unit Johnson S U [16], trapezoidal beta [17] and unit Rayleigh [18] distributions. Many of the above distributions were obtained by transforming the baseline distribution, and they performed better than the beta distribution in terms of data modeling.…”

Section: Introductionmentioning

confidence: 99%

On the Arcsecant Hyperbolic Normal Distribution. Properties, Quantile Regression Modeling and Applications

2021

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This work proposes a new distribution defined on the unit interval. It is obtained by a novel transformation of a normal random variable involving the hyperbolic secant function and its inverse. The use of such a function in distribution theory has not received much attention in the literature, and may be of interest for theoretical and practical purposes. Basic statistical properties of the newly defined distribution are derived, including moments, skewness, kurtosis and order statistics. For the related model, the parametric estimation is examined through different methods. We assess the performance of the obtained estimates by two complementary simulation studies. Also, the quantile regression model based on the proposed distribution is introduced. Applications to three real datasets show that the proposed models are quite competitive in comparison to well-established models.

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Section: Introductionmentioning

confidence: 99%

On the Arcsecant Hyperbolic Normal Distribution. Properties, Quantile Regression Modeling and Applications

2021

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“…The channel noise is appropriately reduced, the curves in Fig. 7c, d decrease first and then increase (anti-unimodal) (Ghitany et al 2019). The optimal ratio x K corresponds to the minimum value.…”

Section: Effects Of Ionic Channel Blockages and Ionic Channel Noise On Discharge Behaviorsmentioning

confidence: 91%

Ionic channel blockage in stochastic Hodgkin–Huxley neuronal model driven by multiple oscillatory signals

Zhou

Wang

et al. 2020

Cogn Neurodyn

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Ionic channel blockage and multiple oscillatory signals play an important role in the dynamical response of pulse sequences. The effects of ionic channel blockage and ionic channel noise on the discharge behaviors are studied in Hodgkin-Huxley neuronal model with multiple oscillatory signals. It is found that bifurcation points of spontaneous discharge are altered through tuning the amplitude of multiple oscillatory signals, and the discharge cycle is changed by increasing the frequency of multiple oscillatory signals. The effects of ionic channel blockage on neural discharge behaviors indicate that the neural excitability can be suppressed by the sodium channel blockage, however, the neural excitability can be reversed by the potassium channel blockage. There is an optimal blockage ratio of potassium channel at which the electrical activity is the most regular, while the order of neural spike is disrupted by the sodium channel blockage. In addition, the frequency of spike discharge is accelerated by increasing the ionic channel noise, the firing of neuron becomes more stable if the ionic channel noise is appropriately reduced. Our results might provide new insights into the effects of ionic channel blockages, multiple oscillatory signals, and ionic channel noises on neural discharge behaviors.

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“…To increase the modeling accuracy of the data sets on (0,1), several distributions have been proposed by researchers. For instance, the unit-Lindley by Mazucheli et al [3], unit-inverse Gaussian by Ghitany et al [4], unit-Birnbaum-Saunders by Mazucheli et al [5], exponentiated Topp-Leone by Pourdarvish et al [6], transmuted Kumaraswamy by Khan et al [7], log-xgamma by Altun and Hamedani [8], log-weighted exponential by Altun [9] and unitimproved second-degree Lindley by Altun and Cordeiro [10].…”

Section: Introductionmentioning

confidence: 99%

A new regression model for bounded response variable: An alternative to the beta and unit-Lindley regression models

2021

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A new distribution defined on (0,1) interval is introduced. Its probability density and cumulative distribution functions have simple forms. Thanks to its simple forms, the moments, incomplete moments and quantile function of the proposed distribution are derived and obtained in explicit forms. Four parameter estimation methods are used to estimate the unknown parameter of the distribution. Besides, simulation study is implemented to compare the efficiencies of these parameter estimation methods. More importantly, owing to the proposed distribution, we provide an alternative regression model for the bounded response variable. The proposed regression model is compared with the beta and unit-Lindley regression models based on two real data sets.

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The unit-inverse Gaussian distribution: A new alternative to two-parameter distributions on the unit interval

Cited by 78 publications

References 29 publications

On the Arcsecant Hyperbolic Normal Distribution. Properties, Quantile Regression Modeling and Applications

On the Arcsecant Hyperbolic Normal Distribution. Properties, Quantile Regression Modeling and Applications

Ionic channel blockage in stochastic Hodgkin–Huxley neuronal model driven by multiple oscillatory signals

A new regression model for bounded response variable: An alternative to the beta and unit-Lindley regression models

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