Finite mixture of inverse Gaussian Distributions {ACDm} R Documentation

## Finite mixture of inverse Gaussian Distribution

### Description

Density (PDF), distribution function (CDF), and hazard function for Finite mixture of inverse Gaussian Distributions.

### Usage

dmixinvgauss(x, theta = .2, lambda = .1, gamma = .05, forceExpectation = F)
pmixinvgauss(q, theta = .2, lambda = .1, gamma = .05, forceExpectation = F)
mixinvgaussHazard(x, theta = .2, lambda = .1, gamma = .05, forceExpectation = F)


### Arguments

 x, q vector of quantiles. theta, lambda, gamma parameters, see 'Details'. forceExpectation logical; if TRUE, the expectation of the distribution is forced to be 1..

### Details

The finite mixture of inverse Gaussian distributions was used by Gomes-Deniz and Perez-Rodrigues (201X) for ACD-models. Its PDF is:

f(x) = \frac{γ + x}{γ + θ} √{\frac{λ}{2 π x^3}} \exp ≤ft[ - \frac{λ(x-θ)^2}{2 x θ^2}\right].

If forceExpectation = TRUE the distribution is transformed by dividing the random variable with its expectation and using the change of variable function.

### References

Gomez-Deniz Perez-Rodriguez (201X) Non-exponential mixtures, non-monotonic financial hazard functions and the autoregressive conditional duration model. Working paper. Retrieved June 16, 2015, from http://dea.uib.es/digitalAssets/254/254084_perez.pdf.

[Package ACDm version 1.0.4 Index]