Results 241 to 250 of about 177,625 (277)

Generalized Inverse Gaussian Distributions and their Wishart Connections

Scandinavian Journal of Statistics, 1998
The matrix generalized inverse Gaussian distribution (MGIG) is shown to arise as a conditional distribution of components of a Wishart distributio n. In the special scalar case, the characterization refers to members of the class of generalized inverse Gaussian distributions (GIGs) and includes the inverse Gaussian distribution among ...
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Generalized MGF of Inverse Gaussian Distribution With Applications to Wireless Communications

IEEE Transactions on Vehicular Technology, 2020
This correspondence considers the inverse Gaussian distribution, which is a tractable and accurate alternative to the log-normal distribution that represents not only shadowing in wireless communications but also turbulence in free-space optical communications.
Jinu Gong, Hoojin Lee, Joonhyuk Kang
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Multivariate distributions with generalized inverse gaussian marginals, and associated poisson mixtures

Canadian Journal of Statistics, 1992
AbstractSeveral types of multivariate extensions of the inverse Gaussian (IG) distribution and the reciprocal inverse Gaussian (RIG) distribution are proposed. Some of these types are obtained as random‐additive‐effect models by means of well‐known convolution properties of the IG and RIG distributions, and they have one‐dimensional IG or RIG marginals.
Barndorff-Nielsen, O. E., Blæsild, P., Seshadri, V.   +2 more
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Study of compound generalized Nakagami–generalized inverse Gaussian distribution and related densities: application to ultrasound imaging

Computational Statistics, 2014
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Gupta, Abhinav, Karmeshu
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Modeling neural activity using the generalized inverse Gaussian distribution

Biological Cybernetics, 1997
Spike trains from neurons are often used to make inferences about the underlying processes that generate the spikes. Random walks or diffusions are commonly used to model these processes; in such models, a spike corresponds to the first passage of the diffusion to a boundary, or firing threshold.
Iyengar, Satish, Liao, Qiming
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A NEW MIXTURE MODEL FROM GENERALIZED POISSON AND GENERALIZED INVERSE GAUSSIAN DISTRIBUTION

Far East Journal of Theoretical Statistics, 2017
Summary: In this paper, we propose a new distribution for modeling count datasets with some unique characteristics, obtained by mixing the generalized Poisson distribution (GPD) and the generalized inverse Gaussian distribution (GIGD) and using the framework of the Lagrangian probability distribution.
Olumoh, J. S., Sanni, O. O. M., Ajayi, O. O., Jolayemi, E. T.   +3 more
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Generalized Gaussian distribution based adaptive mixed-norm inversion for non-Gaussian noise

SEG Technical Program Expanded Abstracts 2015, 2015
We propose an generalized Gaussian distribution based adaptive mixed-norm algorithm to deal with non-Gaussian noise that depend on the precision of the tools used for the measurement and the approximate models for seismic and rock-physics modeling. A mixed-norm functional combines the l1 norm and l2 norm is proposed.
Zhiyong Li*, Zhining Liu, Chengyun Song, Guangmin Hu, Jiashu Zhang   +4 more
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Random variate generation for the generalized inverse Gaussian distribution

Statistics and Computing, 2012
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Infinite divisibility of the hyperbolic and generalized inverse Gaussian distributions

Zeitschrift f�r Wahrscheinlichkeitstheorie und Verwandte Gebiete, 1977
(~'/z)~/2 x~-le --~':~-~+~ (x>0) , (1) 2 K ~ ( ] / ~ ) has the property of infinite divisibility. It follows simply from this that any mixture of the r-dimensional normal distributions Nr(~, X) determined by setting ~ = # + ~ 2 f i A and X=a2A (2) and letting o -2 follow the distribution (1) is infinitely divisible; here #, fl and A are new parameters,
Barndorff-Nielsen, O., Halgreen, Christian   +1 more
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On characterizations of the gamma and generalized inverse Gaussian distributions

Statistics & Probability Letters, 2004
The authors give a simultaneous characterization of generalized inverse Gaussian (GIG) \(\mu_{p,a,b}\) and gamma distributions. The main result is as follows. Assume that the moments \(E(X^{-r-2})\), \(E(X^{-2})\), \(E(Y^r)\) and \(E(Y^{r+2})\) are finite for a fixed \(r\). If the regressions \[ E(V^{r+1}\mid U)=c_r \quad\text{and}\quad E(V^{r+2}\mid U)
Chou, Chao-Wei, Huang, Wen-Jang
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