Finite mixture of inverse Gaussian Distributions | R Documentation |

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

```
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)
```

`x` , `q` |
vector of quantiles. |

`theta` , `lambda` , `gamma` |
parameters, see 'Details'. |

`forceExpectation` |
logical; if |

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{\gamma + x}{\gamma + \theta} \sqrt{\frac{\lambda}{2 \pi x^3}} \exp \left[ - \frac{\lambda(x-\theta)^2}{2 x \theta^2}\right].`

If `forceExpectation = TRUE`

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

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.

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