excessProb.pb {BMAmevt} | R Documentation |

## Estimates the probability of joint excess (Frechet margins)

### Description

Double Monte-Carlo integration.

### Usage

```
excessProb.pb(
post.sample,
Nmin.intern = 100,
precision = 0.05,
from = NULL,
to = NULL,
thin = 100,
displ = FALSE,
thres = rep(500, 5),
known.par = FALSE,
true.par
)
```

### Arguments

`post.sample` |
The posterior sample. |

`Nmin.intern` |
The minimum number of MC iteration in the internal loop (excess probability, conditional to a parameter). |

`precision` |
The desired precision for the internal MC estimate |

`from` |
Integer or |

`to` |
Integer or |

`thin` |
Thinning interval. |

`displ` |
logical. Should a plot be produced ? |

`thres` |
A multivariate threshold |

`known.par` |
Logical |

`true.par` |
The true parameter from which the data are issued. |

### Value

A list made of

- whole
A vector of estimated excess probabilities, one for each element of the thinned posterior sample.

- mean
the estimated threshold excess probability: mean estimate.

- esterr
The estimated standard deviation of the mean estimate (where the Monte-Carlo error is neglected)

- estsd
The estimated standard deviation of the posterior sample (where the Monte-Carlo error is neglected)

- lowquants
The three lower

`0.1`

quantiles of, respectively, the conditional mean estimates and of the upper and lower bounds of the Gaussian (centered)`80`

% confidence intervals around the conditional estimates.- upquants
The three upper

`0.9`

quantiles- true.est
the mean estimate conditional to the true parameter: a vector of size three: the mean estimate , and the latter +/- the standard deviation of the estimate

*BMAmevt*version 1.0.5 Index]