Results 221 to 230 of about 14,957 (259)

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
exaly   +2 more sources

Ultrasonic backscattering in tissue: characterization through Nakagami-generalized inverse Gaussian distribution

Computers in Biology and Medicine, 2007
Ultrasonic tissue characterization through composite probability distributions such as Nakagami-lognormal, Nakagami-gamma, Nakagami-inverse Gaussian has been found to be useful. Such a probabilistic description also depicts heavy tails which arise from multiple scattering in tissue besides local and global variations in scattering cross-sections. A new
R Agrawal
exaly   +3 more sources

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
openaire   +1 more source

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
openaire   +1 more source

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.
Satish Iyengar, Qiming Liao
openaire   +3 more sources

Random variate generation for the generalized inverse Gaussian distribution

Statistics and Computing, 2012
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +1 more source

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
openaire   +2 more sources

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
openaire   +1 more source

Capacity achieving input distribution to the generalized inverse Gaussian neuron model

2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2017
The buildup of a cortical neuron's excitation, called the postsynaptic potential (PSP), is well modeled by the generalized inverse Gaussian (First) Hitting Time (GIGHT) diffusion. Such a model is called the generalized inverse Gaussian (GIG) neuron model. It is also believed that a neuron's purpose is to send information about the state of its input to
Mustafa Sungkar, Toby Berger, William B. Levy   +2 more
openaire   +1 more source

Repeat-Buying and the Generalized Inverse Gaussian-Poisson Distribution

Applied Statistics, 1982
SUMMARY In this article the repeat-buying theory, as originally introduced by Ehrenberg, Chatfield and Goodhardt, has been extended to include, for the mixing of the average purchase levels of individual households, a wider family of distributions, of which the traditional gamma is just a special case.
openaire   +1 more source